This script
import os
import glob
import numpy as np
import pandas as pd
import scipy as sp
from scipy.stats import pearsonr
from scipy.stats import linregress
import seaborn as sns
import matplotlib.pyplot as plt
import re
# Set paths
fcpath = "/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d"
outpath = "~/Desktop/ImageData/PMACS_remote/analysis/postprocessing/"
clinpath = "~/Desktop/ImageData/PMACS_remote/data/clinical"
cestpath = "/Users/pecsok/Desktop/ImageData/PMACS_remote/data/cest/output_measures/UNI/"
# Choose what to analyse
networks = ["SomMot"]
CESTnetworks = ["avgCEST_SomMot", "ctCEST_SomMot"]
CNB_scores = ["tap_tot"]
CNB_valids = ["tap_valid"]
diag_scores = ["hstatus"]
demo_scores = ["sex", "age", "race","ethnic","dateDiff"]
# Make dataframe based on metrics of interest
grp_df = pd.DataFrame(columns = ["BBLID"] + ["Session"] + demo_scores + networks + CESTnetworks + CNB_scores + diag_scores)
print(grp_df)
# Initialize empty lists and vars
bblids = []
sesids = []
# Import group dataframes and set indices
subjlist = pd.read_csv("~/Desktop/ImageData/PMACS_remote/data/subject_list_111623.csv", sep=',')
cnbmat = pd.read_csv(clinpath + "/cnb.csv", sep=',')
diagmat = pd.read_csv(clinpath + "/diagnosis.csv", sep=',')
demomat = pd.read_csv(clinpath + "/demographics.csv", sep=',')
# cestmat = pd.read_csv(clinpath + "/demographics.csv", sep='\t') add grp CEST map here
cnbmat.set_index('bblid', inplace = True)
diagmat.set_index('bblid', inplace = True)
demomat.set_index('bblid', inplace = True)
# Set up renaming dictionary for CEST df
schaefer_indices = pd.read_csv('~/Desktop/ImageData/PMACS_remote/github/glucest-rsfmri/Schaefer2018_100Parcels_17Networks_order_FSLMNI152_2mm.Centroid_RAS.csv', sep=',') # Load the CSV with the mapping of numbers to labels
schaefer_dict = dict(zip(schaefer_indices['ROI Label'], schaefer_indices['ROI Name']))
print("yes")
Empty DataFrame Columns: [BBLID, Session, sex, age, race, ethnic, dateDiff, SomMot, avgCEST_SomMot, ctCEST_SomMot, tap_tot, hstatus] Index: [] yes
runfcon = True
runCNB = True
rundiag = True
rundemo = True
runcest = True
run_grpanalysis = True
FIX THIS ERROR: /var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_77945/3898733492.py:72: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'PSY' has dtype incompatible with float64, please explicitly cast to a compatible dtype first. grp_df.loc[grp_df['BBLID'].astype(str) == bblid, grp_df.columns == diag_score] = diagnosis
# Generates list of all file names
folder_names = [folder for folder in glob.glob(os.path.join(fcpath, "*")) if os.path.isdir(folder)]
#subje
# Loop through subjects
for subj_path in folder_names: # loop through all rows of the spreadsheet
if "sub" in subj_path:
# Extract bblid id:
print(subj_path)
bblid = subj_path.split('-')[1]
print("Processing subject " + bblid)
# Extract session id:
items = os.listdir(subj_path)
ses_folder = [item for item in items if item.startswith("ses")]
ses = ses_folder[0].split('-')[1]
ses_path = os.path.join(fcpath, subj_path, ses_folder[0]) # full path to session
# Add to running list of IDs grp analysis later:
bblids.append(bblid)
sesids.append(ses)
# Start new row in grp_df for this subject:
ids = [bblid, ses] # Values for the first two columns
grp_df.loc[len(grp_df)] = ids + [float('nan')] * (len(grp_df.columns) - len(ids))
print(ses_path)
# Run a subset of subjs or exclude specific subjs.
if bblid != "20902" and bblid != "93242" and bblid != "20754" and bblid != "127065":
##################################################################################################
## FC
##################################################################################################
if runfcon:
os.path.join(fcpath, "sub-" + bblid, "ses-" + ses)
ses_path = os.path.join(fcpath, subj_path, ses_folder[0]) # full path to session
fcmat_glob = f"{ses_path}/func/*Schaefer117_measure-pearsoncorrelation_conmat.tsv"
if os.path.isfile(glob.glob(fcmat_glob)[0]):
fcmat = pd.read_csv(glob.glob(fcmat_glob)[0], sep='\t') # read in fcmat
fcmat.set_index('Node', inplace = True)
# Loop through the networks
for network in networks:
print("Running " + network + " fcon")
# Select rows and columns corresponding to the network
network_fc = fcmat.loc[fcmat.index.str.contains(network), fcmat.columns[fcmat.columns.str.contains(network)]]
# Calculate avg network fc and add value to proper column in grp_df
print(network_fc.values.mean())
grp_df.loc[len(grp_df)-1, network] = network_fc.values.mean()
##################################################################################################
## CNB
##################################################################################################
if runCNB:
# Loop through the CNB scores
for i in range(len(CNB_scores)):
CNB_score = CNB_scores[i]
CNB_valid = CNB_valids[i]
# Select score of interest & validity of that score
scores = cnbmat[CNB_score]
if int(bblid) in scores.index:
score = scores[int(bblid)]
valids = cnbmat[CNB_valid]
valid = str(valids[int(bblid)])
# If score was valid, add to grp_df
if 'V' in valid:
grp_df.loc[grp_df['BBLID'] == bblid, grp_df.columns == CNB_score] = score
##################################################################################################
## Diagnosis
##################################################################################################
if rundiag:
# Loop through the CNB scores
for i in range(len(diag_scores)):
diag_score = diag_scores[i]
# Select score of interest and add to grp_df
diagnoses = diagmat[diag_score]
if int(bblid) in diagnoses.index:
diagnosis = diagnoses[int(bblid)]
grp_df.loc[grp_df['BBLID'].astype(str) == bblid, grp_df.columns == diag_score] = diagnosis
else:
diagnosis = "Unknown"
grp_df.loc[grp_df['BBLID'].astype(str) == bblid, grp_df.columns == diag_score] = diagnosis
##################################################################################################
## Demographics
##################################################################################################
if rundemo:
# Loop through the CNB scores
for i in range(len(demo_scores)):
demo_score = demo_scores[i]
# Select metric of interest
scores = demomat[demo_score]
if int(bblid) in scores.index:
score = scores[int(bblid)]
# Add to grp_df
grp_df.loc[grp_df['BBLID'] == bblid, grp_df.columns == demo_score] = score
##################################################################################################
## CEST
##################################################################################################
if runcest and bblid != "88760" : #88760's CEST output is empty for some reason.
print("Processing " + bblid + "'s CEST data'")
# Extract Glu Session ID
if bblid in subjlist['BBLID'].astype(str).values:
gluses = subjlist.loc[subjlist['BBLID'].astype(str) == bblid, 'SCANID_CEST'].values[0].astype(str) #.
cestid = bblid + "_" + gluses
print(cestid)
# Import data
for network in networks:
cest_pattern = cestpath + cestid + "/" + cestid + "-2d-GluCEST-s100_7-" + network + "-measures_UNI.tsv"
cestfile = glob.glob(cest_pattern)
for file in cestfile:
if os.path.isfile(file):
cestmat = pd.read_csv(file, sep='\t')
means = []
counts = []
col_name = "avgCEST_" + network # for grp_df
ct_name = "ctCEST_" + network # for grp_df
for index, value in enumerate(cestmat.loc[0,:]):
if "Mean" in cestmat.columns[index] and not np.isnan(value):
# cestmat.at[0, cestmat.columns[index]] = float(value) * float(cestmat.iloc[0, index + 1])
means.append(cestmat.at[0, cestmat.columns[index]])
counts.append(cestmat.at[0, cestmat.columns[index + 1]])
if sum(counts) == 0:
grp_df.loc[grp_df['BBLID'] == bblid, grp_df.columns == col_name] = "NaN"
grp_df.loc[grp_df['BBLID'] == bblid, grp_df.columns == col_name] = "NaN"
else:
grp_df.loc[grp_df['BBLID'] == bblid, grp_df.columns == col_name] = sum(means) # / sum(counts)
grp_df.loc[grp_df['BBLID'] == bblid, grp_df.columns == ct_name] = sum(counts) # / sum(counts)
print(grp_df)
# sum_of_mean_columns now contains the sum of values in columns with "Mean" in the column name.
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-19830
Processing subject 19830
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-19830/ses-10789
Running SomMot fcon
0.5043232243880017
Processing 19830's CEST data'
19830_10789
SomMot
[743]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20645
Processing subject 20645
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20645/ses-11260
Running SomMot fcon
0.5720617380987514
Processing 20645's CEST data'
20645_11260
SomMot
[639]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-125511
Processing subject 125511
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-125511/ses-10906
Running SomMot fcon
nan
Processing 125511's CEST data'
125511_10906
SomMot
[535]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-116019
Processing subject 116019
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-116019/ses-11135
Running SomMot fcon
0.476985742874784
Processing 116019's CEST data'
116019_11135
SomMot
[506]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-19790
Processing subject 19790
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-19790/ses-10819
Running SomMot fcon
0.5175497923745217
Processing 19790's CEST data'
19790_10819
SomMot
[598]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-80557
Processing subject 80557
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-80557/ses-10738
Running SomMot fcon
0.5305007860520911
Processing 80557's CEST data'
80557_10738
SomMot
[731]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20642
Processing subject 20642
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20642/ses-11261
Running SomMot fcon
0.47426628025063183
Processing 20642's CEST data'
20642_11261
SomMot
[640]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20180
Processing subject 20180
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20180/ses-11011
Running SomMot fcon
0.20778656869276968
Processing 20180's CEST data'
20180_11011
SomMot
[427]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-139272
Processing subject 139272
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-139272/ses-10739
Running SomMot fcon
0.27218497102444306
Processing 139272's CEST data'
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-121085
Processing subject 121085
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-121085/ses-10851
Running SomMot fcon
0.43996840510989793
Processing 121085's CEST data'
121085_10851
SomMot
[683]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-93292
Processing subject 93292
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-93292/ses-10938
Running SomMot fcon
0.45810106213875146
Processing 93292's CEST data'
93292_10938
SomMot
[563]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-90281
Processing subject 90281
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-90281/ses-10902
Running SomMot fcon
0.49504676313798956
Processing 90281's CEST data'
90281_10902
SomMot
[543]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-96659
Processing subject 96659
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-96659/ses-11096
Running SomMot fcon
0.3630628571620404
Processing 96659's CEST data'
96659_11096
SomMot
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-93274
Processing subject 93274
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-93274/ses-10765
Running SomMot fcon
0.43816497855689907
Processing 93274's CEST data'
93274_10765
SomMot
[707]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-106880
Processing subject 106880
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-106880/ses-10699
Running SomMot fcon
0.33084857768166126
Processing 106880's CEST data'
106880_10699
SomMot
[499]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-90077
Processing subject 90077
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-90077/ses-10962
Running SomMot fcon
0.2762654417352368
Processing 90077's CEST data'
90077_10962
SomMot
[718]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-121407
Processing subject 121407
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-121407/ses-10688
Running SomMot fcon
0.38987538916650333
Processing 121407's CEST data'
121407_10688
SomMot
[716]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-102041
Processing subject 102041
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-102041/ses-10821
Running SomMot fcon
0.5190933819359578
Processing 102041's CEST data'
102041_10675
SomMot
[574]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-119791
Processing subject 119791
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-119791/ses-10705
Running SomMot fcon
0.371118070292174
Processing 119791's CEST data'
119791_10705
SomMot
[613]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-106057
Processing subject 106057
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-106057/ses-11122
Running SomMot fcon
0.6162349104607715
Processing 106057's CEST data'
106057_11122
SomMot
[457]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20011
Processing subject 20011
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20011/ses-10888
Running SomMot fcon
0.5370066728124016
Processing 20011's CEST data'
20011_10888
SomMot
[576]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20543
Processing subject 20543
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20543/ses-11259
Running SomMot fcon
0.5627042385252455
Processing 20543's CEST data'
20543_11259
SomMot
[723]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-87225
Processing subject 87225
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-87225/ses-10933
Running SomMot fcon
0.47038865250074874
Processing 87225's CEST data'
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-19981
Processing subject 19981
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-19981/ses-11106
Running SomMot fcon
0.6256415123250758
Processing 19981's CEST data'
19981_11106
SomMot
[589]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-132641
Processing subject 132641
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-132641/ses-10692
Running SomMot fcon
0.6318741942018791
Processing 132641's CEST data'
132641_10692
SomMot
[720]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-88608
Processing subject 88608
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-88608/ses-10764
Running SomMot fcon
0.32327147547042934
Processing 88608's CEST data'
88608_12108
SomMot
[680]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-97994
Processing subject 97994
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-97994/ses-11114
Running SomMot fcon
0.4575562573424146
Processing 97994's CEST data'
97994_11114
SomMot
[668]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-105979
Processing subject 105979
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-105979/ses-10791
Running SomMot fcon
0.37223398737529784
Processing 105979's CEST data'
105979_10791
SomMot
[640]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-92155
Processing subject 92155
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-92155/ses-11022
Running SomMot fcon
0.3996913347236064
Processing 92155's CEST data'
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-90877
Processing subject 90877
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-90877/ses-10907
Running SomMot fcon
0.5206925022524268
Processing 90877's CEST data'
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-112126
Processing subject 112126
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-112126/ses-11157
Running SomMot fcon
0.2218944305592968
Processing 112126's CEST data'
112126_11157
SomMot
[548]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-89095
Processing subject 89095
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-89095/ses-11100
Running SomMot fcon
0.397562375744611
Processing 89095's CEST data'
89095_11100
SomMot
[755]
BBLID Session sex age race ethnic dateDiff SomMot \
0 19830 10789 2.0 21.92 1.0 2.0 0.0 0.504323
1 20645 11260 1.0 19.84 2.0 2.0 0.0 0.572062
2 125511 10906 2.0 20.86 1.0 2.0 0.0 NaN
3 116019 11135 2.0 25.72 2.0 1.0 0.0 0.476986
4 19790 10819 2.0 23.37 1.0 2.0 0.0 0.517550
5 80557 10738 2.0 29.28 2.0 2.0 0.0 0.530501
6 20642 11261 1.0 25.59 NaN NaN 0.0 0.474266
7 20180 11011 1.0 15.43 1.0 2.0 15.0 0.207787
8 139272 10739 NaN NaN NaN NaN NaN 0.272185
9 121085 10851 2.0 20.84 1.0 2.0 14.0 0.439968
10 93292 10938 2.0 25.32 2.0 2.0 0.0 0.458101
11 90281 10902 2.0 22.89 1.0 2.0 52.0 0.495047
12 96659 11096 1.0 17.74 1.0 2.0 0.0 0.363063
13 93274 10765 2.0 24.64 5.0 2.0 11.0 0.438165
14 106880 10699 1.0 22.62 2.0 2.0 0.0 0.330849
15 90077 10962 2.0 21.34 1.0 2.0 0.0 0.276265
16 121407 10688 2.0 25.78 2.0 2.0 0.0 0.389875
17 102041 10821 1.0 23.43 1.0 2.0 0.0 0.519093
18 119791 10705 1.0 21.70 5.0 2.0 0.0 0.371118
19 106057 11122 2.0 20.12 1.0 2.0 5.0 0.616235
20 20011 10888 2.0 21.42 1.0 2.0 0.0 0.537007
21 20543 11259 1.0 21.51 1.0 1.0 0.0 0.562704
22 87225 10933 NaN NaN NaN NaN NaN 0.470389
23 19981 11106 2.0 23.33 2.0 2.0 0.0 0.625642
24 132641 10692 2.0 23.73 2.0 2.0 0.0 0.631874
25 88608 10764 1.0 25.36 2.0 2.0 1.0 0.323271
26 97994 11114 1.0 19.07 1.0 2.0 0.0 0.457556
27 105979 10791 2.0 24.00 1.0 2.0 0.0 0.372234
28 92155 11022 NaN NaN NaN NaN NaN 0.399691
29 90877 10907 NaN NaN NaN NaN NaN 0.520693
30 112126 11157 2.0 21.83 2.0 2.0 0.0 0.221894
31 89095 11100 2.0 24.62 1.0 2.0 0.0 0.397562
avgCEST_SomMot ctCEST_SomMot tap_tot hstatus
0 7.723693 743.0 NaN NC
1 7.390558 639.0 NaN NaN
2 7.388057 535.0 104.00000 O
3 8.048849 506.0 103.33330 PRO
4 8.941838 598.0 NaN O
5 6.923363 731.0 72.33333 NC
6 8.205851 640.0 NaN NaN
7 8.360912 427.0 92.00000 MDD
8 NaN NaN NaN Unknown
9 8.731863 683.0 NaN NC
10 7.416647 563.0 105.33330 PRO
11 7.336883 543.0 97.00000 NC
12 NaN NaN 95.00000 NC
13 8.874376 707.0 116.33330 PSY
14 7.077155 499.0 92.66666 NC
15 7.627305 718.0 93.33333 PSY
16 6.892677 716.0 117.00000 NC
17 8.036966 574.0 103.33330 NC
18 8.222816 613.0 128.00000 PRO
19 7.866533 457.0 120.00000 PRO
20 8.032503 576.0 NaN PRO
21 8.078890 723.0 NaN MDD
22 NaN NaN NaN Unknown
23 7.778362 589.0 NaN NaN
24 7.681239 720.0 96.33333 PRO
25 7.964384 680.0 110.00000 PRO
26 7.400841 668.0 101.66670 NC
27 7.430732 640.0 92.00000 O
28 NaN NaN NaN Unknown
29 NaN NaN NaN Unknown
30 8.525817 548.0 104.66670 NC
31 8.374417 755.0 125.66670 MDD
# Make a summary table to divide up the cohort by healthy vs psychosis spectrum
if run_grpanalysis:
grp_df['hstatus'] = grp_df['hstatus'].replace('NC', 'HC')
grp_df['hstatus'] = grp_df['hstatus'].replace('PROR', 'PSY')
grp_df['hstatus'] = grp_df['hstatus'].replace('PRO', 'PSY')
grp_df['hstatus'] = grp_df['hstatus'].replace('S', 'PSY')
grp_df['hstatus'] = grp_df['hstatus'].replace('O', 'Other')
grp_df['hstatus'] = grp_df['hstatus'].replace('Unknown', 'Other')
grp_df['hstatus'] = grp_df['hstatus'].replace('MDD', 'Other')
grp_df = grp_df[grp_df['hstatus'] != 'Other']
grp_df = grp_df.dropna() #subset=[network, cestcol, 'hstatus']
value_counts = grp_df['hstatus'].value_counts()
print(grp_df)
print(value_counts)
# Average age (SD)
# Male/female
# Racial demographics
# Psychosis spectrum severity score?
# Medications?
# Comorbidities
BBLID Session sex age race ethnic dateDiff SomMot \
3 116019 11135 2.0 25.72 2.0 1.0 0.0 0.476986
5 80557 10738 2.0 29.28 2.0 2.0 0.0 0.530501
10 93292 10938 2.0 25.32 2.0 2.0 0.0 0.458101
11 90281 10902 2.0 22.89 1.0 2.0 52.0 0.495047
13 93274 10765 2.0 24.64 5.0 2.0 11.0 0.438165
14 106880 10699 1.0 22.62 2.0 2.0 0.0 0.330849
15 90077 10962 2.0 21.34 1.0 2.0 0.0 0.276265
16 121407 10688 2.0 25.78 2.0 2.0 0.0 0.389875
17 102041 10821 1.0 23.43 1.0 2.0 0.0 0.519093
18 119791 10705 1.0 21.70 5.0 2.0 0.0 0.371118
19 106057 11122 2.0 20.12 1.0 2.0 5.0 0.616235
24 132641 10692 2.0 23.73 2.0 2.0 0.0 0.631874
25 88608 10764 1.0 25.36 2.0 2.0 1.0 0.323271
26 97994 11114 1.0 19.07 1.0 2.0 0.0 0.457556
30 112126 11157 2.0 21.83 2.0 2.0 0.0 0.221894
avgCEST_SomMot ctCEST_SomMot tap_tot hstatus
3 8.048849 506.0 103.33330 PSY
5 6.923363 731.0 72.33333 HC
10 7.416647 563.0 105.33330 PSY
11 7.336883 543.0 97.00000 HC
13 8.874376 707.0 116.33330 PSY
14 7.077155 499.0 92.66666 HC
15 7.627305 718.0 93.33333 PSY
16 6.892677 716.0 117.00000 HC
17 8.036966 574.0 103.33330 HC
18 8.222816 613.0 128.00000 PSY
19 7.866533 457.0 120.00000 PSY
24 7.681239 720.0 96.33333 PSY
25 7.964384 680.0 110.00000 PSY
26 7.400841 668.0 101.66670 HC
30 8.525817 548.0 104.66670 HC
hstatus
PSY 8
HC 7
Name: count, dtype: int64
if run_grpanalysis:
colors = pd.DataFrame({'Network': ["Cont", "Default", "DorsAttn", "Vis", "SalVentAttn", "SomMot", "Limbic"],
'Color': ['PuOr', 'PuRd_r', 'PiYG_r', 'PRGn', 'PiYG', 'GnBu_r', 'terrain_r']}) #
# Create a scatter plot with a linear regression line
for network in networks:
cestcol = "avgCEST_" + network
#graph_df = grp_df[grp_df['hstatus'] != 'Other']
graph_df = grp_df
graph_df = graph_df.dropna(subset=[network, cestcol, 'hstatus'])
# Create a linear regression model for fcon
color = colors.loc[colors['Network'] == network, 'Color'].values[0]
sns.set_palette(color)
plot = sns.lmplot(x=network, y=cestcol, data=graph_df, markers= "x") #hue='hstatus',
if network == "SalVentAttn":
plt.xlabel("SN", fontsize=16)
plt.ylabel("avgCEST_SN", fontsize=16)
plt.title('SN FC versus SN GluCEST', fontsize = 20)
else:
plt.xlabel(network, fontsize=16)
plt.ylabel(cestcol, fontsize=16)
plt.title(network + ' FC versus ' + network + ' GluCEST' , fontsize = 20)
# Generate and add slope, r2 and p for subset 1
# slope, intercept, r_value, p_value, std_err = linregress(graph_df.loc[graph_df['hstatus'] == 'PSY', network], graph_df.loc[graph_df['hstatus'] == 'PSY', cestcol])
# plt.text(0.1, 0.8, f'PSY Group\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)
# # Generate and add slope, r2 and p for subset 2
# slope, intercept, r_value, p_value, std_err = linregress(graph_df.loc[graph_df['hstatus'] == 'HC', network], graph_df.loc[graph_df['hstatus'] == 'HC', cestcol])
# plt.text(0.4, 0.8, f'HC Group\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)
# Generate and add slope, r2 and p for all data
slope, intercept, r_value, p_value, std_err = linregress(graph_df[network], graph_df[cestcol])
plt.text(0.1, 0.8, f'All\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)
plt.show()
# Create CNB correlation plot for each network fcon and cest
for CNB_score in CNB_scores:
# graph_df = grp_df[grp_df['hstatus'] != 'Other']
graph_df = grp_df
graph_df = graph_df.dropna(subset=[CNB_score, cestcol, 'hstatus'])
# Add labels and a title to the plot
plot = sns.lmplot(x=cestcol, y=CNB_score,data=graph_df) # hue='hstatus',
if network == "SalVentAttn":
plt.xlabel("avgCEST_SN", fontsize=16)
plt.title('SN GluCEST versus ' + CNB_score, fontsize = 20)
else:
plt.xlabel(cestcol, fontsize=16)
plt.title(network + ' GluCEST versus ' + CNB_score, fontsize = 20)
plt.ylabel(CNB_score, fontsize=16)
# Generate and add slope, r2 and p for subset 1
# slope, intercept, r_value, p_value, std_err = linregress(graph_df.loc[graph_df['hstatus'] == 'PSY', CNB_score], graph_df.loc[graph_df['hstatus'] == 'PSY', cestcol])
# plt.text(0.1, 0.8, f'PSY Group\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)
# Generate and add slope, r2 and p for subset 2
# slope, intercept, r_value, p_value, std_err = linregress(graph_df.loc[graph_df['hstatus'] == 'HC', CNB_score], graph_df.loc[graph_df['hstatus'] == 'HC', cestcol])
# plt.text(0.4, 0.8, f'HC Group\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)
# Generate and add slope, r2 and p for all data
slope, intercept, r_value, p_value, std_err = linregress(graph_df[CNB_score], graph_df[cestcol])
plt.text(0.1, 0.8, f'All\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)
# Show the plot
plt.show()
#graph_df = grp_df[grp_df['hstatus'] != 'Other']
graph_df = grp_df
graph_df = graph_df.dropna(subset=[CNB_score, network, 'hstatus'])
# Add labels and a title to the plot
plot = sns.lmplot(x=network, y=CNB_score, data=graph_df, markers= "s") #hue='hstatus',
if network == "SalVentAttn":
plt.xlabel("SN", fontsize=16)
plt.title('SN FC versus ' + CNB_score, fontsize = 20)
else:
plt.xlabel(network, fontsize=16)
plt.title(network + ' FC versus ' + CNB_score, fontsize = 20)
plt.ylabel(CNB_score, fontsize=16)
# Generate and add slope, r2 and p for subset 1
# slope, intercept, r_value, p_value, std_err = linregress(graph_df.loc[graph_df['hstatus'] == 'PSY', CNB_score], graph_df.loc[graph_df['hstatus'] == 'PSY', network])
# plt.text(0.1, 0.8, f'PSY Group\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)
# Generate and add slope, r2 and p for subset 2
# slope, intercept, r_value, p_value, std_err = linregress(graph_df.loc[graph_df['hstatus'] == 'HC', CNB_score], graph_df.loc[graph_df['hstatus'] == 'HC', network])
# plt.text(0.4, 0.8, f'HC Group\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)
# Generate and add slope, r2 and p for all data
slope, intercept, r_value, p_value, std_err = linregress(graph_df[CNB_score], graph_df[network])
plt.text(0.1, 0.8, f'All\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)
# Show the plot
plt.show()
# Make bar graph comparing diagnostic groups
avg_df = grp_df.groupby('hstatus').agg({cestcol: ['mean', 'std'], network: ['mean', 'std']}).reset_index()
# Flatten the multi-level columns
avg_df.columns = ['_'.join(col).strip() for col in avg_df.columns.values]
print(avg_df)
# Plot CEST bar graph with error bars
sns.barplot(x='hstatus_', y=cestcol + '_mean', data=avg_df, yerr=avg_df[cestcol + '_std'], label='CEST')
plt.xlabel('Diagnostic Group')
plt.ylabel('Mean Value')
plt.ylim(5, 9.5)
plt.title('Average CEST for each hstatus group')
plt.legend()
plt.show()
# Plot fcon bar graph with error bars
sns.barplot(x='hstatus_', y=network + '_mean', data=avg_df, yerr=avg_df[network + '_std'], label='fcon')
plt.xlabel('Diagnostic Group')
plt.ylabel('Mean Value')
plt.title('Average fcon for each hstatus group')
plt.legend()
plt.show()
hstatus
HC 10
Other 10
PSY 9
Name: count, dtype: int64
hstatus
HC 10
Other 10
PSY 9
Name: count, dtype: int64
BBLID Session sex age race ethnic dateDiff Cont Default \
0 19830 10789 2.0 21.92 1.0 2.0 0.0 0.175604 0.376170
1 20645 11260 1.0 19.84 2.0 2.0 0.0 NaN 0.210973
2 125511 10906 2.0 20.86 1.0 2.0 0.0 NaN 0.260692
3 116019 11135 2.0 25.72 2.0 1.0 0.0 0.121458 0.322248
4 19790 10819 2.0 23.37 1.0 2.0 0.0 0.248519 0.331945
5 80557 10738 2.0 29.28 2.0 2.0 0.0 NaN NaN
6 20642 11261 1.0 25.59 NaN NaN 0.0 0.232299 0.216163
7 20180 11011 1.0 15.43 1.0 2.0 15.0 0.096672 0.101490
8 139272 10739 NaN NaN NaN NaN NaN 0.085998 0.153989
9 121085 10851 2.0 20.84 1.0 2.0 14.0 0.201904 0.159492
10 93292 10938 2.0 25.32 2.0 2.0 0.0 0.246400 0.168437
11 90281 10902 2.0 22.89 1.0 2.0 52.0 NaN 0.235045
12 96659 11096 1.0 17.74 1.0 2.0 0.0 0.220468 0.291296
13 93274 10765 2.0 24.64 5.0 2.0 11.0 0.222760 0.233656
14 106880 10699 1.0 22.62 2.0 2.0 0.0 0.341071 0.069097
15 90077 10962 2.0 21.34 1.0 2.0 0.0 0.165071 0.196767
16 121407 10688 2.0 25.78 2.0 2.0 0.0 0.169243 0.402548
17 102041 10821 1.0 23.43 1.0 2.0 0.0 0.261861 0.323492
18 119791 10705 1.0 21.70 5.0 2.0 0.0 0.231845 0.195510
19 106057 11122 2.0 20.12 1.0 2.0 5.0 0.201449 0.240199
20 20011 10888 2.0 21.42 1.0 2.0 0.0 0.259585 0.281621
21 20543 11259 1.0 21.51 1.0 1.0 0.0 0.218721 0.579795
22 87225 10933 NaN NaN NaN NaN NaN NaN 0.230372
23 19981 11106 2.0 23.33 2.0 2.0 0.0 0.150234 0.321884
24 132641 10692 2.0 23.73 2.0 2.0 0.0 0.233773 NaN
25 88608 10764 1.0 25.36 2.0 2.0 1.0 0.201716 0.156970
26 97994 11114 1.0 19.07 1.0 2.0 0.0 0.243740 0.267923
27 105979 10791 2.0 24.00 1.0 2.0 0.0 0.119005 0.399663
28 92155 11022 NaN NaN NaN NaN NaN 0.125406 0.235575
29 90877 10907 NaN NaN NaN NaN NaN 0.114077 0.270397
30 112126 11157 2.0 21.83 2.0 2.0 0.0 0.102177 0.097644
31 89095 11100 2.0 24.62 1.0 2.0 0.0 0.128059 0.364319
DorsAttn ... avgCEST_Vis ctCEST_Vis avgCEST_Limbic ctCEST_Limbic \
0 0.402285 ... 14.297329 175.0 8.867364 391.0
1 0.257396 ... 10.610277 122.0 8.370049 258.0
2 0.258377 ... 8.967306 46.0 10.076208 62.0
3 0.287490 ... 9.610587 194.0 5.855585 267.0
4 0.520766 ... 8.387602 214.0 6.432389 304.0
5 0.316759 ... 12.741945 147.0 10.419856 189.0
6 0.371144 ... 8.465859 293.0 3.779353 162.0
7 0.063928 ... 9.343160 153.0 5.411261 236.0
8 0.241768 ... NaN NaN NaN NaN
9 0.292959 ... 13.178692 110.0 6.876791 116.0
10 0.243422 ... 9.746861 184.0 7.552845 168.0
11 0.429669 ... 10.589238 300.0 6.771436 247.0
12 0.238394 ... NaN NaN NaN NaN
13 0.278428 ... 12.802977 294.0 6.410949 320.0
14 0.176538 ... 13.682704 292.0 5.676007 220.0
15 0.330240 ... 9.176037 161.0 4.421310 533.0
16 0.309288 ... 9.737379 21.0 5.850790 187.0
17 0.510831 ... 10.190264 154.0 6.229536 279.0
18 0.200455 ... 9.714172 242.0 6.640281 229.0
19 0.335207 ... 10.088696 125.0 7.649896 287.0
20 0.294042 ... 11.131604 152.0 7.082764 264.0
21 0.479209 ... 9.479773 27.0 6.994899 147.0
22 0.286440 ... NaN NaN NaN NaN
23 0.218326 ... 13.034999 138.0 5.861445 411.0
24 0.401307 ... 10.633214 128.0 7.509117 298.0
25 0.219597 ... 12.498565 146.0 7.559701 369.0
26 0.494674 ... 7.216132 190.0 4.831669 114.0
27 0.631075 ... 12.373299 162.0 7.654502 258.0
28 0.306728 ... NaN NaN NaN NaN
29 0.251416 ... NaN NaN NaN NaN
30 0.313409 ... 13.772174 114.0 8.371397 73.0
31 0.473277 ... 12.511567 140.0 6.177131 95.0
avgCEST_SalVentAttn ctCEST_SalVentAttn tap_tot er40_cr medf_pc \
0 8.034846 2143.0 NaN 39.0 NaN
1 7.546548 1753.0 NaN NaN NaN
2 7.277624 1245.0 104.00000 38.0 88.888889
3 7.863291 1068.0 103.33330 38.0 77.777778
4 8.805663 1815.0 NaN 39.0 NaN
5 7.181154 1830.0 72.33333 NaN 91.666667
6 8.565783 1634.0 NaN NaN NaN
7 8.124917 1393.0 92.00000 34.0 69.444444
8 NaN NaN NaN NaN NaN
9 8.867149 1667.0 NaN NaN NaN
10 7.937581 1527.0 105.33330 39.0 77.777778
11 7.662420 1683.0 97.00000 39.0 66.666667
12 NaN NaN 95.00000 40.0 83.333333
13 8.939010 1862.0 116.33330 37.0 69.444444
14 7.325671 1855.0 92.66666 NaN 72.222222
15 7.404367 1885.0 93.33333 37.0 80.555556
16 7.365407 1667.0 117.00000 37.0 86.111111
17 7.774126 1705.0 103.33330 36.0 86.111111
18 8.103207 2062.0 128.00000 38.0 66.666667
19 7.989862 1001.0 120.00000 38.0 91.666667
20 8.076560 1678.0 NaN NaN NaN
21 8.118948 1817.0 NaN NaN NaN
22 NaN NaN NaN NaN NaN
23 8.168039 1530.0 NaN NaN NaN
24 7.920916 1806.0 96.33333 40.0 69.444444
25 8.152667 1849.0 110.00000 38.0 88.888889
26 7.594341 1556.0 101.66670 38.0 75.000000
27 8.100647 1514.0 92.00000 40.0 80.555556
28 NaN NaN NaN NaN NaN
29 NaN NaN NaN NaN NaN
30 8.358057 1331.0 104.66670 39.0 83.333333
31 8.547472 1716.0 125.66670 36.0 86.111111
hstatus
0 HC
1 NaN
2 Other
3 PSY
4 Other
5 HC
6 NaN
7 Other
8 Other
9 HC
10 PSY
11 HC
12 HC
13 PSY
14 HC
15 PSY
16 HC
17 HC
18 PSY
19 PSY
20 PSY
21 Other
22 Other
23 NaN
24 PSY
25 PSY
26 HC
27 Other
28 Other
29 Other
30 HC
31 Other
[32 rows x 32 columns]
hstatus_ avgCEST_Cont_mean avgCEST_Cont_std Cont_mean Cont_std 0 HC 8.185614 0.499726 0.214508 0.071204 1 Other 8.067074 0.188945 0.142057 0.058799 2 PSY 8.444832 0.392330 0.209340 0.043280
BBLID Session sex age race ethnic dateDiff Cont Default \
0 19830 10789 2.0 21.92 1.0 2.0 0.0 0.175604 0.376170
1 20645 11260 1.0 19.84 2.0 2.0 0.0 NaN 0.210973
2 125511 10906 2.0 20.86 1.0 2.0 0.0 NaN 0.260692
3 116019 11135 2.0 25.72 2.0 1.0 0.0 0.121458 0.322248
4 19790 10819 2.0 23.37 1.0 2.0 0.0 0.248519 0.331945
5 80557 10738 2.0 29.28 2.0 2.0 0.0 NaN NaN
6 20642 11261 1.0 25.59 NaN NaN 0.0 0.232299 0.216163
7 20180 11011 1.0 15.43 1.0 2.0 15.0 0.096672 0.101490
8 139272 10739 NaN NaN NaN NaN NaN 0.085998 0.153989
9 121085 10851 2.0 20.84 1.0 2.0 14.0 0.201904 0.159492
10 93292 10938 2.0 25.32 2.0 2.0 0.0 0.246400 0.168437
11 90281 10902 2.0 22.89 1.0 2.0 52.0 NaN 0.235045
12 96659 11096 1.0 17.74 1.0 2.0 0.0 0.220468 0.291296
13 93274 10765 2.0 24.64 5.0 2.0 11.0 0.222760 0.233656
14 106880 10699 1.0 22.62 2.0 2.0 0.0 0.341071 0.069097
15 90077 10962 2.0 21.34 1.0 2.0 0.0 0.165071 0.196767
16 121407 10688 2.0 25.78 2.0 2.0 0.0 0.169243 0.402548
17 102041 10821 1.0 23.43 1.0 2.0 0.0 0.261861 0.323492
18 119791 10705 1.0 21.70 5.0 2.0 0.0 0.231845 0.195510
19 106057 11122 2.0 20.12 1.0 2.0 5.0 0.201449 0.240199
20 20011 10888 2.0 21.42 1.0 2.0 0.0 0.259585 0.281621
21 20543 11259 1.0 21.51 1.0 1.0 0.0 0.218721 0.579795
22 87225 10933 NaN NaN NaN NaN NaN NaN 0.230372
23 19981 11106 2.0 23.33 2.0 2.0 0.0 0.150234 0.321884
24 132641 10692 2.0 23.73 2.0 2.0 0.0 0.233773 NaN
25 88608 10764 1.0 25.36 2.0 2.0 1.0 0.201716 0.156970
26 97994 11114 1.0 19.07 1.0 2.0 0.0 0.243740 0.267923
27 105979 10791 2.0 24.00 1.0 2.0 0.0 0.119005 0.399663
28 92155 11022 NaN NaN NaN NaN NaN 0.125406 0.235575
29 90877 10907 NaN NaN NaN NaN NaN 0.114077 0.270397
30 112126 11157 2.0 21.83 2.0 2.0 0.0 0.102177 0.097644
31 89095 11100 2.0 24.62 1.0 2.0 0.0 0.128059 0.364319
DorsAttn ... avgCEST_Vis ctCEST_Vis avgCEST_Limbic ctCEST_Limbic \
0 0.402285 ... 14.297329 175.0 8.867364 391.0
1 0.257396 ... 10.610277 122.0 8.370049 258.0
2 0.258377 ... 8.967306 46.0 10.076208 62.0
3 0.287490 ... 9.610587 194.0 5.855585 267.0
4 0.520766 ... 8.387602 214.0 6.432389 304.0
5 0.316759 ... 12.741945 147.0 10.419856 189.0
6 0.371144 ... 8.465859 293.0 3.779353 162.0
7 0.063928 ... 9.343160 153.0 5.411261 236.0
8 0.241768 ... NaN NaN NaN NaN
9 0.292959 ... 13.178692 110.0 6.876791 116.0
10 0.243422 ... 9.746861 184.0 7.552845 168.0
11 0.429669 ... 10.589238 300.0 6.771436 247.0
12 0.238394 ... NaN NaN NaN NaN
13 0.278428 ... 12.802977 294.0 6.410949 320.0
14 0.176538 ... 13.682704 292.0 5.676007 220.0
15 0.330240 ... 9.176037 161.0 4.421310 533.0
16 0.309288 ... 9.737379 21.0 5.850790 187.0
17 0.510831 ... 10.190264 154.0 6.229536 279.0
18 0.200455 ... 9.714172 242.0 6.640281 229.0
19 0.335207 ... 10.088696 125.0 7.649896 287.0
20 0.294042 ... 11.131604 152.0 7.082764 264.0
21 0.479209 ... 9.479773 27.0 6.994899 147.0
22 0.286440 ... NaN NaN NaN NaN
23 0.218326 ... 13.034999 138.0 5.861445 411.0
24 0.401307 ... 10.633214 128.0 7.509117 298.0
25 0.219597 ... 12.498565 146.0 7.559701 369.0
26 0.494674 ... 7.216132 190.0 4.831669 114.0
27 0.631075 ... 12.373299 162.0 7.654502 258.0
28 0.306728 ... NaN NaN NaN NaN
29 0.251416 ... NaN NaN NaN NaN
30 0.313409 ... 13.772174 114.0 8.371397 73.0
31 0.473277 ... 12.511567 140.0 6.177131 95.0
avgCEST_SalVentAttn ctCEST_SalVentAttn tap_tot er40_cr medf_pc \
0 8.034846 2143.0 NaN 39.0 NaN
1 7.546548 1753.0 NaN NaN NaN
2 7.277624 1245.0 104.00000 38.0 88.888889
3 7.863291 1068.0 103.33330 38.0 77.777778
4 8.805663 1815.0 NaN 39.0 NaN
5 7.181154 1830.0 72.33333 NaN 91.666667
6 8.565783 1634.0 NaN NaN NaN
7 8.124917 1393.0 92.00000 34.0 69.444444
8 NaN NaN NaN NaN NaN
9 8.867149 1667.0 NaN NaN NaN
10 7.937581 1527.0 105.33330 39.0 77.777778
11 7.662420 1683.0 97.00000 39.0 66.666667
12 NaN NaN 95.00000 40.0 83.333333
13 8.939010 1862.0 116.33330 37.0 69.444444
14 7.325671 1855.0 92.66666 NaN 72.222222
15 7.404367 1885.0 93.33333 37.0 80.555556
16 7.365407 1667.0 117.00000 37.0 86.111111
17 7.774126 1705.0 103.33330 36.0 86.111111
18 8.103207 2062.0 128.00000 38.0 66.666667
19 7.989862 1001.0 120.00000 38.0 91.666667
20 8.076560 1678.0 NaN NaN NaN
21 8.118948 1817.0 NaN NaN NaN
22 NaN NaN NaN NaN NaN
23 8.168039 1530.0 NaN NaN NaN
24 7.920916 1806.0 96.33333 40.0 69.444444
25 8.152667 1849.0 110.00000 38.0 88.888889
26 7.594341 1556.0 101.66670 38.0 75.000000
27 8.100647 1514.0 92.00000 40.0 80.555556
28 NaN NaN NaN NaN NaN
29 NaN NaN NaN NaN NaN
30 8.358057 1331.0 104.66670 39.0 83.333333
31 8.547472 1716.0 125.66670 36.0 86.111111
hstatus
0 HC
1 NaN
2 Other
3 PSY
4 Other
5 HC
6 NaN
7 Other
8 Other
9 HC
10 PSY
11 HC
12 HC
13 PSY
14 HC
15 PSY
16 HC
17 HC
18 PSY
19 PSY
20 PSY
21 Other
22 Other
23 NaN
24 PSY
25 PSY
26 HC
27 Other
28 Other
29 Other
30 HC
31 Other
[32 rows x 32 columns]
hstatus_ avgCEST_Default_mean avgCEST_Default_std Default_mean \ 0 HC 7.823633 0.416928 0.246967 1 Other 7.742404 0.405308 0.292824 2 PSY 7.817142 0.321882 0.224426 Default_std 0 0.117733 1 0.135403 2 0.056645
BBLID Session sex age race ethnic dateDiff Cont Default \
0 19830 10789 2.0 21.92 1.0 2.0 0.0 0.175604 0.376170
1 20645 11260 1.0 19.84 2.0 2.0 0.0 NaN 0.210973
2 125511 10906 2.0 20.86 1.0 2.0 0.0 NaN 0.260692
3 116019 11135 2.0 25.72 2.0 1.0 0.0 0.121458 0.322248
4 19790 10819 2.0 23.37 1.0 2.0 0.0 0.248519 0.331945
5 80557 10738 2.0 29.28 2.0 2.0 0.0 NaN NaN
6 20642 11261 1.0 25.59 NaN NaN 0.0 0.232299 0.216163
7 20180 11011 1.0 15.43 1.0 2.0 15.0 0.096672 0.101490
8 139272 10739 NaN NaN NaN NaN NaN 0.085998 0.153989
9 121085 10851 2.0 20.84 1.0 2.0 14.0 0.201904 0.159492
10 93292 10938 2.0 25.32 2.0 2.0 0.0 0.246400 0.168437
11 90281 10902 2.0 22.89 1.0 2.0 52.0 NaN 0.235045
12 96659 11096 1.0 17.74 1.0 2.0 0.0 0.220468 0.291296
13 93274 10765 2.0 24.64 5.0 2.0 11.0 0.222760 0.233656
14 106880 10699 1.0 22.62 2.0 2.0 0.0 0.341071 0.069097
15 90077 10962 2.0 21.34 1.0 2.0 0.0 0.165071 0.196767
16 121407 10688 2.0 25.78 2.0 2.0 0.0 0.169243 0.402548
17 102041 10821 1.0 23.43 1.0 2.0 0.0 0.261861 0.323492
18 119791 10705 1.0 21.70 5.0 2.0 0.0 0.231845 0.195510
19 106057 11122 2.0 20.12 1.0 2.0 5.0 0.201449 0.240199
20 20011 10888 2.0 21.42 1.0 2.0 0.0 0.259585 0.281621
21 20543 11259 1.0 21.51 1.0 1.0 0.0 0.218721 0.579795
22 87225 10933 NaN NaN NaN NaN NaN NaN 0.230372
23 19981 11106 2.0 23.33 2.0 2.0 0.0 0.150234 0.321884
24 132641 10692 2.0 23.73 2.0 2.0 0.0 0.233773 NaN
25 88608 10764 1.0 25.36 2.0 2.0 1.0 0.201716 0.156970
26 97994 11114 1.0 19.07 1.0 2.0 0.0 0.243740 0.267923
27 105979 10791 2.0 24.00 1.0 2.0 0.0 0.119005 0.399663
28 92155 11022 NaN NaN NaN NaN NaN 0.125406 0.235575
29 90877 10907 NaN NaN NaN NaN NaN 0.114077 0.270397
30 112126 11157 2.0 21.83 2.0 2.0 0.0 0.102177 0.097644
31 89095 11100 2.0 24.62 1.0 2.0 0.0 0.128059 0.364319
DorsAttn ... avgCEST_Vis ctCEST_Vis avgCEST_Limbic ctCEST_Limbic \
0 0.402285 ... 14.297329 175.0 8.867364 391.0
1 0.257396 ... 10.610277 122.0 8.370049 258.0
2 0.258377 ... 8.967306 46.0 10.076208 62.0
3 0.287490 ... 9.610587 194.0 5.855585 267.0
4 0.520766 ... 8.387602 214.0 6.432389 304.0
5 0.316759 ... 12.741945 147.0 10.419856 189.0
6 0.371144 ... 8.465859 293.0 3.779353 162.0
7 0.063928 ... 9.343160 153.0 5.411261 236.0
8 0.241768 ... NaN NaN NaN NaN
9 0.292959 ... 13.178692 110.0 6.876791 116.0
10 0.243422 ... 9.746861 184.0 7.552845 168.0
11 0.429669 ... 10.589238 300.0 6.771436 247.0
12 0.238394 ... NaN NaN NaN NaN
13 0.278428 ... 12.802977 294.0 6.410949 320.0
14 0.176538 ... 13.682704 292.0 5.676007 220.0
15 0.330240 ... 9.176037 161.0 4.421310 533.0
16 0.309288 ... 9.737379 21.0 5.850790 187.0
17 0.510831 ... 10.190264 154.0 6.229536 279.0
18 0.200455 ... 9.714172 242.0 6.640281 229.0
19 0.335207 ... 10.088696 125.0 7.649896 287.0
20 0.294042 ... 11.131604 152.0 7.082764 264.0
21 0.479209 ... 9.479773 27.0 6.994899 147.0
22 0.286440 ... NaN NaN NaN NaN
23 0.218326 ... 13.034999 138.0 5.861445 411.0
24 0.401307 ... 10.633214 128.0 7.509117 298.0
25 0.219597 ... 12.498565 146.0 7.559701 369.0
26 0.494674 ... 7.216132 190.0 4.831669 114.0
27 0.631075 ... 12.373299 162.0 7.654502 258.0
28 0.306728 ... NaN NaN NaN NaN
29 0.251416 ... NaN NaN NaN NaN
30 0.313409 ... 13.772174 114.0 8.371397 73.0
31 0.473277 ... 12.511567 140.0 6.177131 95.0
avgCEST_SalVentAttn ctCEST_SalVentAttn tap_tot er40_cr medf_pc \
0 8.034846 2143.0 NaN 39.0 NaN
1 7.546548 1753.0 NaN NaN NaN
2 7.277624 1245.0 104.00000 38.0 88.888889
3 7.863291 1068.0 103.33330 38.0 77.777778
4 8.805663 1815.0 NaN 39.0 NaN
5 7.181154 1830.0 72.33333 NaN 91.666667
6 8.565783 1634.0 NaN NaN NaN
7 8.124917 1393.0 92.00000 34.0 69.444444
8 NaN NaN NaN NaN NaN
9 8.867149 1667.0 NaN NaN NaN
10 7.937581 1527.0 105.33330 39.0 77.777778
11 7.662420 1683.0 97.00000 39.0 66.666667
12 NaN NaN 95.00000 40.0 83.333333
13 8.939010 1862.0 116.33330 37.0 69.444444
14 7.325671 1855.0 92.66666 NaN 72.222222
15 7.404367 1885.0 93.33333 37.0 80.555556
16 7.365407 1667.0 117.00000 37.0 86.111111
17 7.774126 1705.0 103.33330 36.0 86.111111
18 8.103207 2062.0 128.00000 38.0 66.666667
19 7.989862 1001.0 120.00000 38.0 91.666667
20 8.076560 1678.0 NaN NaN NaN
21 8.118948 1817.0 NaN NaN NaN
22 NaN NaN NaN NaN NaN
23 8.168039 1530.0 NaN NaN NaN
24 7.920916 1806.0 96.33333 40.0 69.444444
25 8.152667 1849.0 110.00000 38.0 88.888889
26 7.594341 1556.0 101.66670 38.0 75.000000
27 8.100647 1514.0 92.00000 40.0 80.555556
28 NaN NaN NaN NaN NaN
29 NaN NaN NaN NaN NaN
30 8.358057 1331.0 104.66670 39.0 83.333333
31 8.547472 1716.0 125.66670 36.0 86.111111
hstatus
0 HC
1 NaN
2 Other
3 PSY
4 Other
5 HC
6 NaN
7 Other
8 Other
9 HC
10 PSY
11 HC
12 HC
13 PSY
14 HC
15 PSY
16 HC
17 HC
18 PSY
19 PSY
20 PSY
21 Other
22 Other
23 NaN
24 PSY
25 PSY
26 HC
27 Other
28 Other
29 Other
30 HC
31 Other
[32 rows x 32 columns]
hstatus_ avgCEST_DorsAttn_mean avgCEST_DorsAttn_std DorsAttn_mean \ 0 HC 7.531305 0.721082 0.348481 1 Other 8.203656 0.968130 0.351299 2 PSY 7.981276 1.204224 0.287799 DorsAttn_std 0 0.108400 1 0.169187 2 0.062560
BBLID Session sex age race ethnic dateDiff Cont Default \
0 19830 10789 2.0 21.92 1.0 2.0 0.0 0.175604 0.376170
1 20645 11260 1.0 19.84 2.0 2.0 0.0 NaN 0.210973
2 125511 10906 2.0 20.86 1.0 2.0 0.0 NaN 0.260692
3 116019 11135 2.0 25.72 2.0 1.0 0.0 0.121458 0.322248
4 19790 10819 2.0 23.37 1.0 2.0 0.0 0.248519 0.331945
5 80557 10738 2.0 29.28 2.0 2.0 0.0 NaN NaN
6 20642 11261 1.0 25.59 NaN NaN 0.0 0.232299 0.216163
7 20180 11011 1.0 15.43 1.0 2.0 15.0 0.096672 0.101490
8 139272 10739 NaN NaN NaN NaN NaN 0.085998 0.153989
9 121085 10851 2.0 20.84 1.0 2.0 14.0 0.201904 0.159492
10 93292 10938 2.0 25.32 2.0 2.0 0.0 0.246400 0.168437
11 90281 10902 2.0 22.89 1.0 2.0 52.0 NaN 0.235045
12 96659 11096 1.0 17.74 1.0 2.0 0.0 0.220468 0.291296
13 93274 10765 2.0 24.64 5.0 2.0 11.0 0.222760 0.233656
14 106880 10699 1.0 22.62 2.0 2.0 0.0 0.341071 0.069097
15 90077 10962 2.0 21.34 1.0 2.0 0.0 0.165071 0.196767
16 121407 10688 2.0 25.78 2.0 2.0 0.0 0.169243 0.402548
17 102041 10821 1.0 23.43 1.0 2.0 0.0 0.261861 0.323492
18 119791 10705 1.0 21.70 5.0 2.0 0.0 0.231845 0.195510
19 106057 11122 2.0 20.12 1.0 2.0 5.0 0.201449 0.240199
20 20011 10888 2.0 21.42 1.0 2.0 0.0 0.259585 0.281621
21 20543 11259 1.0 21.51 1.0 1.0 0.0 0.218721 0.579795
22 87225 10933 NaN NaN NaN NaN NaN NaN 0.230372
23 19981 11106 2.0 23.33 2.0 2.0 0.0 0.150234 0.321884
24 132641 10692 2.0 23.73 2.0 2.0 0.0 0.233773 NaN
25 88608 10764 1.0 25.36 2.0 2.0 1.0 0.201716 0.156970
26 97994 11114 1.0 19.07 1.0 2.0 0.0 0.243740 0.267923
27 105979 10791 2.0 24.00 1.0 2.0 0.0 0.119005 0.399663
28 92155 11022 NaN NaN NaN NaN NaN 0.125406 0.235575
29 90877 10907 NaN NaN NaN NaN NaN 0.114077 0.270397
30 112126 11157 2.0 21.83 2.0 2.0 0.0 0.102177 0.097644
31 89095 11100 2.0 24.62 1.0 2.0 0.0 0.128059 0.364319
DorsAttn ... avgCEST_Vis ctCEST_Vis avgCEST_Limbic ctCEST_Limbic \
0 0.402285 ... 14.297329 175.0 8.867364 391.0
1 0.257396 ... 10.610277 122.0 8.370049 258.0
2 0.258377 ... 8.967306 46.0 10.076208 62.0
3 0.287490 ... 9.610587 194.0 5.855585 267.0
4 0.520766 ... 8.387602 214.0 6.432389 304.0
5 0.316759 ... 12.741945 147.0 10.419856 189.0
6 0.371144 ... 8.465859 293.0 3.779353 162.0
7 0.063928 ... 9.343160 153.0 5.411261 236.0
8 0.241768 ... NaN NaN NaN NaN
9 0.292959 ... 13.178692 110.0 6.876791 116.0
10 0.243422 ... 9.746861 184.0 7.552845 168.0
11 0.429669 ... 10.589238 300.0 6.771436 247.0
12 0.238394 ... NaN NaN NaN NaN
13 0.278428 ... 12.802977 294.0 6.410949 320.0
14 0.176538 ... 13.682704 292.0 5.676007 220.0
15 0.330240 ... 9.176037 161.0 4.421310 533.0
16 0.309288 ... 9.737379 21.0 5.850790 187.0
17 0.510831 ... 10.190264 154.0 6.229536 279.0
18 0.200455 ... 9.714172 242.0 6.640281 229.0
19 0.335207 ... 10.088696 125.0 7.649896 287.0
20 0.294042 ... 11.131604 152.0 7.082764 264.0
21 0.479209 ... 9.479773 27.0 6.994899 147.0
22 0.286440 ... NaN NaN NaN NaN
23 0.218326 ... 13.034999 138.0 5.861445 411.0
24 0.401307 ... 10.633214 128.0 7.509117 298.0
25 0.219597 ... 12.498565 146.0 7.559701 369.0
26 0.494674 ... 7.216132 190.0 4.831669 114.0
27 0.631075 ... 12.373299 162.0 7.654502 258.0
28 0.306728 ... NaN NaN NaN NaN
29 0.251416 ... NaN NaN NaN NaN
30 0.313409 ... 13.772174 114.0 8.371397 73.0
31 0.473277 ... 12.511567 140.0 6.177131 95.0
avgCEST_SalVentAttn ctCEST_SalVentAttn tap_tot er40_cr medf_pc \
0 8.034846 2143.0 NaN 39.0 NaN
1 7.546548 1753.0 NaN NaN NaN
2 7.277624 1245.0 104.00000 38.0 88.888889
3 7.863291 1068.0 103.33330 38.0 77.777778
4 8.805663 1815.0 NaN 39.0 NaN
5 7.181154 1830.0 72.33333 NaN 91.666667
6 8.565783 1634.0 NaN NaN NaN
7 8.124917 1393.0 92.00000 34.0 69.444444
8 NaN NaN NaN NaN NaN
9 8.867149 1667.0 NaN NaN NaN
10 7.937581 1527.0 105.33330 39.0 77.777778
11 7.662420 1683.0 97.00000 39.0 66.666667
12 NaN NaN 95.00000 40.0 83.333333
13 8.939010 1862.0 116.33330 37.0 69.444444
14 7.325671 1855.0 92.66666 NaN 72.222222
15 7.404367 1885.0 93.33333 37.0 80.555556
16 7.365407 1667.0 117.00000 37.0 86.111111
17 7.774126 1705.0 103.33330 36.0 86.111111
18 8.103207 2062.0 128.00000 38.0 66.666667
19 7.989862 1001.0 120.00000 38.0 91.666667
20 8.076560 1678.0 NaN NaN NaN
21 8.118948 1817.0 NaN NaN NaN
22 NaN NaN NaN NaN NaN
23 8.168039 1530.0 NaN NaN NaN
24 7.920916 1806.0 96.33333 40.0 69.444444
25 8.152667 1849.0 110.00000 38.0 88.888889
26 7.594341 1556.0 101.66670 38.0 75.000000
27 8.100647 1514.0 92.00000 40.0 80.555556
28 NaN NaN NaN NaN NaN
29 NaN NaN NaN NaN NaN
30 8.358057 1331.0 104.66670 39.0 83.333333
31 8.547472 1716.0 125.66670 36.0 86.111111
hstatus
0 HC
1 NaN
2 Other
3 PSY
4 Other
5 HC
6 NaN
7 Other
8 Other
9 HC
10 PSY
11 HC
12 HC
13 PSY
14 HC
15 PSY
16 HC
17 HC
18 PSY
19 PSY
20 PSY
21 Other
22 Other
23 NaN
24 PSY
25 PSY
26 HC
27 Other
28 Other
29 Other
30 HC
31 Other
[32 rows x 32 columns]
hstatus_ avgCEST_Vis_mean avgCEST_Vis_std Vis_mean Vis_std 0 HC 11.711762 2.390401 0.448846 0.207738 1 Other 10.177118 1.795586 0.565973 0.185518 2 PSY 10.600301 1.300093 0.516471 0.167178
BBLID Session sex age race ethnic dateDiff Cont Default \
0 19830 10789 2.0 21.92 1.0 2.0 0.0 0.175604 0.376170
1 20645 11260 1.0 19.84 2.0 2.0 0.0 NaN 0.210973
2 125511 10906 2.0 20.86 1.0 2.0 0.0 NaN 0.260692
3 116019 11135 2.0 25.72 2.0 1.0 0.0 0.121458 0.322248
4 19790 10819 2.0 23.37 1.0 2.0 0.0 0.248519 0.331945
5 80557 10738 2.0 29.28 2.0 2.0 0.0 NaN NaN
6 20642 11261 1.0 25.59 NaN NaN 0.0 0.232299 0.216163
7 20180 11011 1.0 15.43 1.0 2.0 15.0 0.096672 0.101490
8 139272 10739 NaN NaN NaN NaN NaN 0.085998 0.153989
9 121085 10851 2.0 20.84 1.0 2.0 14.0 0.201904 0.159492
10 93292 10938 2.0 25.32 2.0 2.0 0.0 0.246400 0.168437
11 90281 10902 2.0 22.89 1.0 2.0 52.0 NaN 0.235045
12 96659 11096 1.0 17.74 1.0 2.0 0.0 0.220468 0.291296
13 93274 10765 2.0 24.64 5.0 2.0 11.0 0.222760 0.233656
14 106880 10699 1.0 22.62 2.0 2.0 0.0 0.341071 0.069097
15 90077 10962 2.0 21.34 1.0 2.0 0.0 0.165071 0.196767
16 121407 10688 2.0 25.78 2.0 2.0 0.0 0.169243 0.402548
17 102041 10821 1.0 23.43 1.0 2.0 0.0 0.261861 0.323492
18 119791 10705 1.0 21.70 5.0 2.0 0.0 0.231845 0.195510
19 106057 11122 2.0 20.12 1.0 2.0 5.0 0.201449 0.240199
20 20011 10888 2.0 21.42 1.0 2.0 0.0 0.259585 0.281621
21 20543 11259 1.0 21.51 1.0 1.0 0.0 0.218721 0.579795
22 87225 10933 NaN NaN NaN NaN NaN NaN 0.230372
23 19981 11106 2.0 23.33 2.0 2.0 0.0 0.150234 0.321884
24 132641 10692 2.0 23.73 2.0 2.0 0.0 0.233773 NaN
25 88608 10764 1.0 25.36 2.0 2.0 1.0 0.201716 0.156970
26 97994 11114 1.0 19.07 1.0 2.0 0.0 0.243740 0.267923
27 105979 10791 2.0 24.00 1.0 2.0 0.0 0.119005 0.399663
28 92155 11022 NaN NaN NaN NaN NaN 0.125406 0.235575
29 90877 10907 NaN NaN NaN NaN NaN 0.114077 0.270397
30 112126 11157 2.0 21.83 2.0 2.0 0.0 0.102177 0.097644
31 89095 11100 2.0 24.62 1.0 2.0 0.0 0.128059 0.364319
DorsAttn ... avgCEST_Vis ctCEST_Vis avgCEST_Limbic ctCEST_Limbic \
0 0.402285 ... 14.297329 175.0 8.867364 391.0
1 0.257396 ... 10.610277 122.0 8.370049 258.0
2 0.258377 ... 8.967306 46.0 10.076208 62.0
3 0.287490 ... 9.610587 194.0 5.855585 267.0
4 0.520766 ... 8.387602 214.0 6.432389 304.0
5 0.316759 ... 12.741945 147.0 10.419856 189.0
6 0.371144 ... 8.465859 293.0 3.779353 162.0
7 0.063928 ... 9.343160 153.0 5.411261 236.0
8 0.241768 ... NaN NaN NaN NaN
9 0.292959 ... 13.178692 110.0 6.876791 116.0
10 0.243422 ... 9.746861 184.0 7.552845 168.0
11 0.429669 ... 10.589238 300.0 6.771436 247.0
12 0.238394 ... NaN NaN NaN NaN
13 0.278428 ... 12.802977 294.0 6.410949 320.0
14 0.176538 ... 13.682704 292.0 5.676007 220.0
15 0.330240 ... 9.176037 161.0 4.421310 533.0
16 0.309288 ... 9.737379 21.0 5.850790 187.0
17 0.510831 ... 10.190264 154.0 6.229536 279.0
18 0.200455 ... 9.714172 242.0 6.640281 229.0
19 0.335207 ... 10.088696 125.0 7.649896 287.0
20 0.294042 ... 11.131604 152.0 7.082764 264.0
21 0.479209 ... 9.479773 27.0 6.994899 147.0
22 0.286440 ... NaN NaN NaN NaN
23 0.218326 ... 13.034999 138.0 5.861445 411.0
24 0.401307 ... 10.633214 128.0 7.509117 298.0
25 0.219597 ... 12.498565 146.0 7.559701 369.0
26 0.494674 ... 7.216132 190.0 4.831669 114.0
27 0.631075 ... 12.373299 162.0 7.654502 258.0
28 0.306728 ... NaN NaN NaN NaN
29 0.251416 ... NaN NaN NaN NaN
30 0.313409 ... 13.772174 114.0 8.371397 73.0
31 0.473277 ... 12.511567 140.0 6.177131 95.0
avgCEST_SalVentAttn ctCEST_SalVentAttn tap_tot er40_cr medf_pc \
0 8.034846 2143.0 NaN 39.0 NaN
1 7.546548 1753.0 NaN NaN NaN
2 7.277624 1245.0 104.00000 38.0 88.888889
3 7.863291 1068.0 103.33330 38.0 77.777778
4 8.805663 1815.0 NaN 39.0 NaN
5 7.181154 1830.0 72.33333 NaN 91.666667
6 8.565783 1634.0 NaN NaN NaN
7 8.124917 1393.0 92.00000 34.0 69.444444
8 NaN NaN NaN NaN NaN
9 8.867149 1667.0 NaN NaN NaN
10 7.937581 1527.0 105.33330 39.0 77.777778
11 7.662420 1683.0 97.00000 39.0 66.666667
12 NaN NaN 95.00000 40.0 83.333333
13 8.939010 1862.0 116.33330 37.0 69.444444
14 7.325671 1855.0 92.66666 NaN 72.222222
15 7.404367 1885.0 93.33333 37.0 80.555556
16 7.365407 1667.0 117.00000 37.0 86.111111
17 7.774126 1705.0 103.33330 36.0 86.111111
18 8.103207 2062.0 128.00000 38.0 66.666667
19 7.989862 1001.0 120.00000 38.0 91.666667
20 8.076560 1678.0 NaN NaN NaN
21 8.118948 1817.0 NaN NaN NaN
22 NaN NaN NaN NaN NaN
23 8.168039 1530.0 NaN NaN NaN
24 7.920916 1806.0 96.33333 40.0 69.444444
25 8.152667 1849.0 110.00000 38.0 88.888889
26 7.594341 1556.0 101.66670 38.0 75.000000
27 8.100647 1514.0 92.00000 40.0 80.555556
28 NaN NaN NaN NaN NaN
29 NaN NaN NaN NaN NaN
30 8.358057 1331.0 104.66670 39.0 83.333333
31 8.547472 1716.0 125.66670 36.0 86.111111
hstatus
0 HC
1 NaN
2 Other
3 PSY
4 Other
5 HC
6 NaN
7 Other
8 Other
9 HC
10 PSY
11 HC
12 HC
13 PSY
14 HC
15 PSY
16 HC
17 HC
18 PSY
19 PSY
20 PSY
21 Other
22 Other
23 NaN
24 PSY
25 PSY
26 HC
27 Other
28 Other
29 Other
30 HC
31 Other
[32 rows x 32 columns]
hstatus_ avgCEST_SalVentAttn_mean avgCEST_SalVentAttn_std \ 0 HC 7.795908 0.543594 1 Other 8.162545 0.520001 2 PSY 8.043051 0.401342 SalVentAttn_mean SalVentAttn_std 0 0.368396 0.106964 1 0.329335 0.081924 2 0.366513 0.084872
BBLID Session sex age race ethnic dateDiff Cont Default \
0 19830 10789 2.0 21.92 1.0 2.0 0.0 0.175604 0.376170
1 20645 11260 1.0 19.84 2.0 2.0 0.0 NaN 0.210973
2 125511 10906 2.0 20.86 1.0 2.0 0.0 NaN 0.260692
3 116019 11135 2.0 25.72 2.0 1.0 0.0 0.121458 0.322248
4 19790 10819 2.0 23.37 1.0 2.0 0.0 0.248519 0.331945
5 80557 10738 2.0 29.28 2.0 2.0 0.0 NaN NaN
6 20642 11261 1.0 25.59 NaN NaN 0.0 0.232299 0.216163
7 20180 11011 1.0 15.43 1.0 2.0 15.0 0.096672 0.101490
8 139272 10739 NaN NaN NaN NaN NaN 0.085998 0.153989
9 121085 10851 2.0 20.84 1.0 2.0 14.0 0.201904 0.159492
10 93292 10938 2.0 25.32 2.0 2.0 0.0 0.246400 0.168437
11 90281 10902 2.0 22.89 1.0 2.0 52.0 NaN 0.235045
12 96659 11096 1.0 17.74 1.0 2.0 0.0 0.220468 0.291296
13 93274 10765 2.0 24.64 5.0 2.0 11.0 0.222760 0.233656
14 106880 10699 1.0 22.62 2.0 2.0 0.0 0.341071 0.069097
15 90077 10962 2.0 21.34 1.0 2.0 0.0 0.165071 0.196767
16 121407 10688 2.0 25.78 2.0 2.0 0.0 0.169243 0.402548
17 102041 10821 1.0 23.43 1.0 2.0 0.0 0.261861 0.323492
18 119791 10705 1.0 21.70 5.0 2.0 0.0 0.231845 0.195510
19 106057 11122 2.0 20.12 1.0 2.0 5.0 0.201449 0.240199
20 20011 10888 2.0 21.42 1.0 2.0 0.0 0.259585 0.281621
21 20543 11259 1.0 21.51 1.0 1.0 0.0 0.218721 0.579795
22 87225 10933 NaN NaN NaN NaN NaN NaN 0.230372
23 19981 11106 2.0 23.33 2.0 2.0 0.0 0.150234 0.321884
24 132641 10692 2.0 23.73 2.0 2.0 0.0 0.233773 NaN
25 88608 10764 1.0 25.36 2.0 2.0 1.0 0.201716 0.156970
26 97994 11114 1.0 19.07 1.0 2.0 0.0 0.243740 0.267923
27 105979 10791 2.0 24.00 1.0 2.0 0.0 0.119005 0.399663
28 92155 11022 NaN NaN NaN NaN NaN 0.125406 0.235575
29 90877 10907 NaN NaN NaN NaN NaN 0.114077 0.270397
30 112126 11157 2.0 21.83 2.0 2.0 0.0 0.102177 0.097644
31 89095 11100 2.0 24.62 1.0 2.0 0.0 0.128059 0.364319
DorsAttn ... avgCEST_Vis ctCEST_Vis avgCEST_Limbic ctCEST_Limbic \
0 0.402285 ... 14.297329 175.0 8.867364 391.0
1 0.257396 ... 10.610277 122.0 8.370049 258.0
2 0.258377 ... 8.967306 46.0 10.076208 62.0
3 0.287490 ... 9.610587 194.0 5.855585 267.0
4 0.520766 ... 8.387602 214.0 6.432389 304.0
5 0.316759 ... 12.741945 147.0 10.419856 189.0
6 0.371144 ... 8.465859 293.0 3.779353 162.0
7 0.063928 ... 9.343160 153.0 5.411261 236.0
8 0.241768 ... NaN NaN NaN NaN
9 0.292959 ... 13.178692 110.0 6.876791 116.0
10 0.243422 ... 9.746861 184.0 7.552845 168.0
11 0.429669 ... 10.589238 300.0 6.771436 247.0
12 0.238394 ... NaN NaN NaN NaN
13 0.278428 ... 12.802977 294.0 6.410949 320.0
14 0.176538 ... 13.682704 292.0 5.676007 220.0
15 0.330240 ... 9.176037 161.0 4.421310 533.0
16 0.309288 ... 9.737379 21.0 5.850790 187.0
17 0.510831 ... 10.190264 154.0 6.229536 279.0
18 0.200455 ... 9.714172 242.0 6.640281 229.0
19 0.335207 ... 10.088696 125.0 7.649896 287.0
20 0.294042 ... 11.131604 152.0 7.082764 264.0
21 0.479209 ... 9.479773 27.0 6.994899 147.0
22 0.286440 ... NaN NaN NaN NaN
23 0.218326 ... 13.034999 138.0 5.861445 411.0
24 0.401307 ... 10.633214 128.0 7.509117 298.0
25 0.219597 ... 12.498565 146.0 7.559701 369.0
26 0.494674 ... 7.216132 190.0 4.831669 114.0
27 0.631075 ... 12.373299 162.0 7.654502 258.0
28 0.306728 ... NaN NaN NaN NaN
29 0.251416 ... NaN NaN NaN NaN
30 0.313409 ... 13.772174 114.0 8.371397 73.0
31 0.473277 ... 12.511567 140.0 6.177131 95.0
avgCEST_SalVentAttn ctCEST_SalVentAttn tap_tot er40_cr medf_pc \
0 8.034846 2143.0 NaN 39.0 NaN
1 7.546548 1753.0 NaN NaN NaN
2 7.277624 1245.0 104.00000 38.0 88.888889
3 7.863291 1068.0 103.33330 38.0 77.777778
4 8.805663 1815.0 NaN 39.0 NaN
5 7.181154 1830.0 72.33333 NaN 91.666667
6 8.565783 1634.0 NaN NaN NaN
7 8.124917 1393.0 92.00000 34.0 69.444444
8 NaN NaN NaN NaN NaN
9 8.867149 1667.0 NaN NaN NaN
10 7.937581 1527.0 105.33330 39.0 77.777778
11 7.662420 1683.0 97.00000 39.0 66.666667
12 NaN NaN 95.00000 40.0 83.333333
13 8.939010 1862.0 116.33330 37.0 69.444444
14 7.325671 1855.0 92.66666 NaN 72.222222
15 7.404367 1885.0 93.33333 37.0 80.555556
16 7.365407 1667.0 117.00000 37.0 86.111111
17 7.774126 1705.0 103.33330 36.0 86.111111
18 8.103207 2062.0 128.00000 38.0 66.666667
19 7.989862 1001.0 120.00000 38.0 91.666667
20 8.076560 1678.0 NaN NaN NaN
21 8.118948 1817.0 NaN NaN NaN
22 NaN NaN NaN NaN NaN
23 8.168039 1530.0 NaN NaN NaN
24 7.920916 1806.0 96.33333 40.0 69.444444
25 8.152667 1849.0 110.00000 38.0 88.888889
26 7.594341 1556.0 101.66670 38.0 75.000000
27 8.100647 1514.0 92.00000 40.0 80.555556
28 NaN NaN NaN NaN NaN
29 NaN NaN NaN NaN NaN
30 8.358057 1331.0 104.66670 39.0 83.333333
31 8.547472 1716.0 125.66670 36.0 86.111111
hstatus
0 HC
1 NaN
2 Other
3 PSY
4 Other
5 HC
6 NaN
7 Other
8 Other
9 HC
10 PSY
11 HC
12 HC
13 PSY
14 HC
15 PSY
16 HC
17 HC
18 PSY
19 PSY
20 PSY
21 Other
22 Other
23 NaN
24 PSY
25 PSY
26 HC
27 Other
28 Other
29 Other
30 HC
31 Other
[32 rows x 32 columns]
hstatus_ avgCEST_SomMot_mean avgCEST_SomMot_std SomMot_mean SomMot_std 0 HC 7.627695 0.678048 0.425217 0.098687 1 Other 8.095808 0.601198 0.413422 0.118454 2 PSY 7.970517 0.419776 0.458780 0.122978
BBLID Session sex age race ethnic dateDiff Cont Default \
0 19830 10789 2.0 21.92 1.0 2.0 0.0 0.175604 0.376170
1 20645 11260 1.0 19.84 2.0 2.0 0.0 NaN 0.210973
2 125511 10906 2.0 20.86 1.0 2.0 0.0 NaN 0.260692
3 116019 11135 2.0 25.72 2.0 1.0 0.0 0.121458 0.322248
4 19790 10819 2.0 23.37 1.0 2.0 0.0 0.248519 0.331945
5 80557 10738 2.0 29.28 2.0 2.0 0.0 NaN NaN
6 20642 11261 1.0 25.59 NaN NaN 0.0 0.232299 0.216163
7 20180 11011 1.0 15.43 1.0 2.0 15.0 0.096672 0.101490
8 139272 10739 NaN NaN NaN NaN NaN 0.085998 0.153989
9 121085 10851 2.0 20.84 1.0 2.0 14.0 0.201904 0.159492
10 93292 10938 2.0 25.32 2.0 2.0 0.0 0.246400 0.168437
11 90281 10902 2.0 22.89 1.0 2.0 52.0 NaN 0.235045
12 96659 11096 1.0 17.74 1.0 2.0 0.0 0.220468 0.291296
13 93274 10765 2.0 24.64 5.0 2.0 11.0 0.222760 0.233656
14 106880 10699 1.0 22.62 2.0 2.0 0.0 0.341071 0.069097
15 90077 10962 2.0 21.34 1.0 2.0 0.0 0.165071 0.196767
16 121407 10688 2.0 25.78 2.0 2.0 0.0 0.169243 0.402548
17 102041 10821 1.0 23.43 1.0 2.0 0.0 0.261861 0.323492
18 119791 10705 1.0 21.70 5.0 2.0 0.0 0.231845 0.195510
19 106057 11122 2.0 20.12 1.0 2.0 5.0 0.201449 0.240199
20 20011 10888 2.0 21.42 1.0 2.0 0.0 0.259585 0.281621
21 20543 11259 1.0 21.51 1.0 1.0 0.0 0.218721 0.579795
22 87225 10933 NaN NaN NaN NaN NaN NaN 0.230372
23 19981 11106 2.0 23.33 2.0 2.0 0.0 0.150234 0.321884
24 132641 10692 2.0 23.73 2.0 2.0 0.0 0.233773 NaN
25 88608 10764 1.0 25.36 2.0 2.0 1.0 0.201716 0.156970
26 97994 11114 1.0 19.07 1.0 2.0 0.0 0.243740 0.267923
27 105979 10791 2.0 24.00 1.0 2.0 0.0 0.119005 0.399663
28 92155 11022 NaN NaN NaN NaN NaN 0.125406 0.235575
29 90877 10907 NaN NaN NaN NaN NaN 0.114077 0.270397
30 112126 11157 2.0 21.83 2.0 2.0 0.0 0.102177 0.097644
31 89095 11100 2.0 24.62 1.0 2.0 0.0 0.128059 0.364319
DorsAttn ... avgCEST_Vis ctCEST_Vis avgCEST_Limbic ctCEST_Limbic \
0 0.402285 ... 14.297329 175.0 8.867364 391.0
1 0.257396 ... 10.610277 122.0 8.370049 258.0
2 0.258377 ... 8.967306 46.0 10.076208 62.0
3 0.287490 ... 9.610587 194.0 5.855585 267.0
4 0.520766 ... 8.387602 214.0 6.432389 304.0
5 0.316759 ... 12.741945 147.0 10.419856 189.0
6 0.371144 ... 8.465859 293.0 3.779353 162.0
7 0.063928 ... 9.343160 153.0 5.411261 236.0
8 0.241768 ... NaN NaN NaN NaN
9 0.292959 ... 13.178692 110.0 6.876791 116.0
10 0.243422 ... 9.746861 184.0 7.552845 168.0
11 0.429669 ... 10.589238 300.0 6.771436 247.0
12 0.238394 ... NaN NaN NaN NaN
13 0.278428 ... 12.802977 294.0 6.410949 320.0
14 0.176538 ... 13.682704 292.0 5.676007 220.0
15 0.330240 ... 9.176037 161.0 4.421310 533.0
16 0.309288 ... 9.737379 21.0 5.850790 187.0
17 0.510831 ... 10.190264 154.0 6.229536 279.0
18 0.200455 ... 9.714172 242.0 6.640281 229.0
19 0.335207 ... 10.088696 125.0 7.649896 287.0
20 0.294042 ... 11.131604 152.0 7.082764 264.0
21 0.479209 ... 9.479773 27.0 6.994899 147.0
22 0.286440 ... NaN NaN NaN NaN
23 0.218326 ... 13.034999 138.0 5.861445 411.0
24 0.401307 ... 10.633214 128.0 7.509117 298.0
25 0.219597 ... 12.498565 146.0 7.559701 369.0
26 0.494674 ... 7.216132 190.0 4.831669 114.0
27 0.631075 ... 12.373299 162.0 7.654502 258.0
28 0.306728 ... NaN NaN NaN NaN
29 0.251416 ... NaN NaN NaN NaN
30 0.313409 ... 13.772174 114.0 8.371397 73.0
31 0.473277 ... 12.511567 140.0 6.177131 95.0
avgCEST_SalVentAttn ctCEST_SalVentAttn tap_tot er40_cr medf_pc \
0 8.034846 2143.0 NaN 39.0 NaN
1 7.546548 1753.0 NaN NaN NaN
2 7.277624 1245.0 104.00000 38.0 88.888889
3 7.863291 1068.0 103.33330 38.0 77.777778
4 8.805663 1815.0 NaN 39.0 NaN
5 7.181154 1830.0 72.33333 NaN 91.666667
6 8.565783 1634.0 NaN NaN NaN
7 8.124917 1393.0 92.00000 34.0 69.444444
8 NaN NaN NaN NaN NaN
9 8.867149 1667.0 NaN NaN NaN
10 7.937581 1527.0 105.33330 39.0 77.777778
11 7.662420 1683.0 97.00000 39.0 66.666667
12 NaN NaN 95.00000 40.0 83.333333
13 8.939010 1862.0 116.33330 37.0 69.444444
14 7.325671 1855.0 92.66666 NaN 72.222222
15 7.404367 1885.0 93.33333 37.0 80.555556
16 7.365407 1667.0 117.00000 37.0 86.111111
17 7.774126 1705.0 103.33330 36.0 86.111111
18 8.103207 2062.0 128.00000 38.0 66.666667
19 7.989862 1001.0 120.00000 38.0 91.666667
20 8.076560 1678.0 NaN NaN NaN
21 8.118948 1817.0 NaN NaN NaN
22 NaN NaN NaN NaN NaN
23 8.168039 1530.0 NaN NaN NaN
24 7.920916 1806.0 96.33333 40.0 69.444444
25 8.152667 1849.0 110.00000 38.0 88.888889
26 7.594341 1556.0 101.66670 38.0 75.000000
27 8.100647 1514.0 92.00000 40.0 80.555556
28 NaN NaN NaN NaN NaN
29 NaN NaN NaN NaN NaN
30 8.358057 1331.0 104.66670 39.0 83.333333
31 8.547472 1716.0 125.66670 36.0 86.111111
hstatus
0 HC
1 NaN
2 Other
3 PSY
4 Other
5 HC
6 NaN
7 Other
8 Other
9 HC
10 PSY
11 HC
12 HC
13 PSY
14 HC
15 PSY
16 HC
17 HC
18 PSY
19 PSY
20 PSY
21 Other
22 Other
23 NaN
24 PSY
25 PSY
26 HC
27 Other
28 Other
29 Other
30 HC
31 Other
[32 rows x 32 columns]
hstatus_ avgCEST_Limbic_mean avgCEST_Limbic_std Limbic_mean Limbic_std 0 HC 7.099427 1.782223 0.347572 0.028164 1 Other 7.124398 1.632299 0.458920 0.178272 2 PSY 6.742494 1.069243 0.475692 0.100969
hstatus
NC 23
PRO 17
Unknown 6
S 6
PSY 4
O 2
PROR 2
MDD 2
Name: count, dtype: int64
hstatus
PSY 29
HC 23
Other 10
Name: count, dtype: int64
Intercept: 98.15734353062948
Coefficients: avgCEST_Cont 2.697674
Cont -59.573144
dtype: float64
Cont-tap_tot ANOVA Results:
sum_sq df F PR(>F)
avgCEST_Cont 65.282549 1.0 0.470055 0.497478
Cont 620.575015 1.0 4.468335 0.041727
Residual 4860.899631 35.0 NaN NaN
Intercept: 29.333477223416942
Coefficients: avgCEST_Cont 1.077393
Cont -3.241797
dtype: float64
Cont-er40_cr ANOVA Results:
sum_sq df F PR(>F)
avgCEST_Cont 9.940285 1.0 2.200472 0.14718
Cont 1.800830 1.0 0.398648 0.53201
Residual 153.589643 34.0 NaN NaN
Intercept: 115.79929901723546
Coefficients: avgCEST_Cont -4.534770
Cont 7.701735
dtype: float64
Cont-medf_pc ANOVA Results:
sum_sq df F PR(>F)
avgCEST_Cont 184.950421 1.0 2.530715 0.120394
Cont 10.394443 1.0 0.142229 0.708289
Residual 2630.962333 36.0 NaN NaN
Intercept: 122.74213574519109
Coefficients: avgCEST_Default -1.758623
Default -7.896795
dtype: float64
Default-tap_tot ANOVA Results:
sum_sq df F PR(>F)
avgCEST_Default 35.643597 1.0 0.229076 0.635186
Default 14.469524 1.0 0.092994 0.762212
Residual 5445.899572 35.0 NaN NaN
Intercept: 32.20208139781507
Coefficients: avgCEST_Default 0.622225
Default 1.329105
dtype: float64
Default-er40_cr ANOVA Results:
sum_sq df F PR(>F)
avgCEST_Default 4.853881 1.0 1.028229 0.317534
Default 0.442673 1.0 0.093774 0.761247
Residual 165.221754 35.0 NaN NaN
Intercept: 100.17272823532824
Coefficients: avgCEST_Default -2.232662
Default -6.430613
dtype: float64
Default-medf_pc ANOVA Results:
sum_sq df F PR(>F)
avgCEST_Default 64.185954 1.0 0.867039 0.357813
Default 10.478025 1.0 0.141540 0.708904
Residual 2739.069952 37.0 NaN NaN
Intercept: 98.48317557492753
Coefficients: avgCEST_DorsAttn 1.608352
DorsAttn -11.853633
dtype: float64
DorsAttn-tap_tot ANOVA Results:
sum_sq df F PR(>F)
avgCEST_DorsAttn 127.291965 1.0 0.822087 0.370952
DorsAttn 57.925357 1.0 0.374098 0.544847
Residual 5264.561248 34.0 NaN NaN
Intercept: 34.49962067296072
Coefficients: avgCEST_DorsAttn 0.343066
DorsAttn 0.388906
dtype: float64
DorsAttn-er40_cr ANOVA Results:
sum_sq df F PR(>F)
avgCEST_DorsAttn 5.404832 1.0 1.111523 0.299186
DorsAttn 0.055687 1.0 0.011452 0.915406
Residual 165.326548 34.0 NaN NaN
Intercept: 76.58981050313344
Coefficients: avgCEST_DorsAttn -0.426495
DorsAttn 20.814416
dtype: float64
DorsAttn-medf_pc ANOVA Results:
sum_sq df F PR(>F)
avgCEST_DorsAttn 8.972726 1.0 0.124570 0.726188
DorsAttn 181.624474 1.0 2.521525 0.121047
Residual 2593.065856 36.0 NaN NaN
Intercept: 121.05404292285397
Coefficients: avgCEST_Vis -1.345877
Vis 0.567614
dtype: float64
Vis-tap_tot ANOVA Results:
sum_sq df F PR(>F)
avgCEST_Vis 558.293498 1.0 4.004387 0.053184
Vis 0.112394 1.0 0.000806 0.977510
Residual 4879.716656 35.0 NaN NaN
Intercept: 31.156963939739214
Coefficients: avgCEST_Vis 0.297392
Vis 6.045508
dtype: float64
Vis-er40_cr ANOVA Results:
sum_sq df F PR(>F)
avgCEST_Vis 28.298582 1.0 7.293807 0.010590
Vis 14.206267 1.0 3.661589 0.063888
Residual 135.793333 35.0 NaN NaN
Intercept: 75.38431425667916
Coefficients: avgCEST_Vis -0.396246
Vis 19.487511
dtype: float64
Vis-medf_pc ANOVA Results:
sum_sq df F PR(>F)
avgCEST_Vis 51.852995 1.0 0.747893 0.392717
Vis 148.350756 1.0 2.139711 0.151972
Residual 2565.288933 37.0 NaN NaN
Intercept: 88.13929846429112
Coefficients: avgCEST_SalVentAttn 4.252572
SalVentAttn -36.026225
dtype: float64
SalVentAttn-tap_tot ANOVA Results:
sum_sq df F PR(>F)
avgCEST_SalVentAttn 318.842198 1.0 2.348598 0.134386
SalVentAttn 331.952370 1.0 2.445168 0.126885
Residual 4751.548507 35.0 NaN NaN
Intercept: 35.30826117361063
Coefficients: avgCEST_SalVentAttn 0.139538
SalVentAttn 2.490422
dtype: float64
SalVentAttn-er40_cr ANOVA Results:
sum_sq df F PR(>F)
avgCEST_SalVentAttn 0.341213 1.0 0.070517 0.792144
SalVentAttn 1.491608 1.0 0.308263 0.582280
Residual 169.356536 35.0 NaN NaN
Intercept: 79.11838145735956
Coefficients: avgCEST_SalVentAttn -0.400522
SalVentAttn 12.462481
dtype: float64
SalVentAttn-medf_pc ANOVA Results:
sum_sq df F PR(>F)
avgCEST_SalVentAttn 2.880867 1.0 0.038398 0.845717
SalVentAttn 39.893415 1.0 0.531731 0.470471
Residual 2775.946235 37.0 NaN NaN
Intercept: 91.70496871655598
Coefficients: avgCEST_SomMot 5.991925
SomMot -58.524134
dtype: float64
SomMot-tap_tot ANOVA Results:
sum_sq df F PR(>F)
avgCEST_SomMot 835.774243 1.0 7.040765 0.011901
SomMot 927.078511 1.0 7.809934 0.008375
Residual 4154.676386 35.0 NaN NaN
Intercept: 39.05182884112296
Coefficients: avgCEST_SomMot -0.092637
SomMot -1.815914
dtype: float64
SomMot-er40_cr ANOVA Results:
sum_sq df F PR(>F)
avgCEST_SomMot 0.202848 1.0 0.041878 0.839038
SomMot 0.940487 1.0 0.194162 0.662184
Residual 169.533512 35.0 NaN NaN
Intercept: 76.20130955735688
Coefficients: avgCEST_SomMot -0.294569
SomMot 13.412113
dtype: float64
SomMot-medf_pc ANOVA Results:
sum_sq df F PR(>F)
avgCEST_SomMot 2.153254 1.0 0.028807 0.866150
SomMot 56.371411 1.0 0.754156 0.390762
Residual 2765.663941 37.0 NaN NaN
Intercept: 106.10749624874113
Coefficients: avgCEST_Limbic -2.511216
Limbic 33.444777
dtype: float64
Limbic-tap_tot ANOVA Results:
sum_sq df F PR(>F)
avgCEST_Limbic 342.287811 1.0 1.840251 0.191693
Limbic 329.034270 1.0 1.768995 0.200115
Residual 3348.011802 18.0 NaN NaN
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
Intercept: 33.82998607543426
Coefficients: avgCEST_Limbic 0.551864
Limbic 0.235921
dtype: float64
Limbic-er40_cr ANOVA Results:
sum_sq df F PR(>F)
avgCEST_Limbic 11.949987 1.0 3.055763 0.099610
Limbic 0.015180 1.0 0.003882 0.951094
Residual 62.570223 16.0 NaN NaN
Intercept: 97.47546461686645
Coefficients: avgCEST_Limbic -0.436728
Limbic -31.264380
dtype: float64
Limbic-medf_pc ANOVA Results:
sum_sq df F PR(>F)
avgCEST_Limbic 10.352499 1.0 0.137690 0.714920
Limbic 287.530662 1.0 3.824205 0.066223
Residual 1353.366817 18.0 NaN NaN
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
print('Intercept:', model.params[0])
#CLUNKIER COMPREHENSIVE VERSION:
from sklearn import linear_model
import statsmodels.api as sm
import statsmodels.formula.api as smf
if run_grpanalysis:
# Curate data
value_counts = grp_df['hstatus'].value_counts()
print(value_counts)
grp_df['hstatus'] = grp_df['hstatus'].replace('NC', 'HC')
grp_df['hstatus'] = grp_df['hstatus'].replace('PROR', 'PSY')
grp_df['hstatus'] = grp_df['hstatus'].replace('PRO', 'PSY')
grp_df['hstatus'] = grp_df['hstatus'].replace('S', 'PSY')
grp_df['hstatus'] = grp_df['hstatus'].replace('O', 'Other')
grp_df['hstatus'] = grp_df['hstatus'].replace('Unknown', 'Other')
grp_df['hstatus'] = grp_df['hstatus'].replace('MDD', 'Other')
value_counts = grp_df['hstatus'].value_counts()
print(value_counts)
colors = pd.DataFrame({'Network': ["Cont", "Default", "DorsAttn", "Vis", "SalVentAttn", "SomMot", "Limbic"],
'Color': ['PuOr', 'PuRd_r', 'PiYG_r', 'PRGn', 'PiYG', 'GnBu_r', 'terrain_r']}) #
anova_tables = []
# Create a scatter plot with a multiple linear regression
for network in networks:
cestcol = "avgCEST_" + network
# Create a linear regression model for fcon
# color = colors.loc[colors['Network'] == network, 'Color'].values[0]
# sns.set_palette(color)
# Create CNB correlation plot for each network fcon and cest
for CNB_score in CNB_scores:
# graph_df = grp_df[grp_df['hstatus'] != 'Other']
graph_df = grp_df
graph_df = graph_df.dropna(subset=[CNB_score, cestcol, network])
graph_df = graph_df[[CNB_score, cestcol, network]]
# Define x values and target variable
X = graph_df[[cestcol, network]]
Y = graph_df[CNB_score]
##################################
# Define formula and model
formula = f'{CNB_score} ~ {cestcol} + {network}'
model = smf.ols(formula=formula, data=graph_df).fit()
print('\n\n\n' + network + ' & ' + CNB_score)
print(model.summary())
# Plotting the regression line
fig, ax = plt.subplots()
ax.scatter(graph_df[cestcol], graph_df[CNB_score], label='Actual Data')
# Generate x values for the line
x_line = pd.DataFrame({cestcol: np.linspace(graph_df[cestcol].min(), graph_df[cestcol].max(), 100),
network: np.mean(graph_df[network])}) # Use mean value for the network variable
# Predictions for the regression line
y_line = model.predict(x_line)
# Plot the regression line
ax.plot(x_line[cestcol], y_line, color='red', label='Regression Line')
ax.set_xlabel(cestcol)
ax.set_ylabel(CNB_score)
ax.set_title('Multiple Linear Regression Plot for ' + CNB_score + ' & ' + network)
ax.legend()
plt.show()
hstatus
NC 10
PRO 7
Unknown 4
O 3
MDD 3
PSY 2
Name: count, dtype: int64
hstatus
HC 10
Other 10
PSY 9
Name: count, dtype: int64
Cont & tap_tot
OLS Regression Results
==============================================================================
Dep. Variable: tap_tot R-squared: 0.024
Model: OLS Adj. R-squared: -0.126
Method: Least Squares F-statistic: 0.1631
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.851
Time: 19:10:01 Log-Likelihood: -61.774
No. Observations: 16 AIC: 129.5
Df Residuals: 13 BIC: 131.9
Df Model: 2
Covariance Type: nonrobust
================================================================================
coef std err t P>|t| [0.025 0.975]
--------------------------------------------------------------------------------
Intercept 67.5854 68.496 0.987 0.342 -80.391 215.562
avgCEST_Cont 4.5797 8.019 0.571 0.578 -12.744 21.904
Cont 4.4999 48.771 0.092 0.928 -100.863 109.863
==============================================================================
Omnibus: 2.237 Durbin-Watson: 1.733
Prob(Omnibus): 0.327 Jarque-Bera (JB): 1.628
Skew: 0.614 Prob(JB): 0.443
Kurtosis: 2.034 Cond. No. 187.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
Cont & er40_cr
OLS Regression Results
==============================================================================
Dep. Variable: er40_cr R-squared: 0.058
Model: OLS Adj. R-squared: -0.076
Method: Least Squares F-statistic: 0.4322
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.657
Time: 19:10:02 Log-Likelihood: -30.555
No. Observations: 17 AIC: 67.11
Df Residuals: 14 BIC: 69.61
Df Model: 2
Covariance Type: nonrobust
================================================================================
coef std err t P>|t| [0.025 0.975]
--------------------------------------------------------------------------------
Intercept 33.6264 9.145 3.677 0.002 14.013 53.240
avgCEST_Cont 0.3706 1.101 0.337 0.741 -1.991 2.732
Cont 5.9738 7.173 0.833 0.419 -9.412 21.359
==============================================================================
Omnibus: 0.570 Durbin-Watson: 1.979
Prob(Omnibus): 0.752 Jarque-Bera (JB): 0.322
Skew: -0.315 Prob(JB): 0.851
Kurtosis: 2.763 Cond. No. 198.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=17
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
Cont & medf_pc
OLS Regression Results
==============================================================================
Dep. Variable: medf_pc R-squared: 0.140
Model: OLS Adj. R-squared: 0.008
Method: Least Squares F-statistic: 1.059
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.375
Time: 19:10:02 Log-Likelihood: -53.945
No. Observations: 16 AIC: 113.9
Df Residuals: 13 BIC: 116.2
Df Model: 2
Covariance Type: nonrobust
================================================================================
coef std err t P>|t| [0.025 0.975]
--------------------------------------------------------------------------------
Intercept 130.7061 41.990 3.113 0.008 39.992 221.420
avgCEST_Cont -5.5129 4.916 -1.121 0.282 -16.133 5.107
Cont -32.4703 29.898 -1.086 0.297 -97.061 32.120
==============================================================================
Omnibus: 0.229 Durbin-Watson: 2.831
Prob(Omnibus): 0.892 Jarque-Bera (JB): 0.406
Skew: -0.178 Prob(JB): 0.816
Kurtosis: 2.306 Cond. No. 187.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
Default & tap_tot
OLS Regression Results
==============================================================================
Dep. Variable: tap_tot R-squared: 0.135
Model: OLS Adj. R-squared: 0.011
Method: Least Squares F-statistic: 1.088
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.364
Time: 19:10:02 Log-Likelihood: -64.073
No. Observations: 17 AIC: 134.1
Df Residuals: 14 BIC: 136.6
Df Model: 2
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 156.9385 57.839 2.713 0.017 32.887 280.990
avgCEST_Default -7.5511 7.482 -1.009 0.330 -23.597 8.495
Default 34.8139 28.622 1.216 0.244 -26.575 96.203
==============================================================================
Omnibus: 0.974 Durbin-Watson: 1.809
Prob(Omnibus): 0.615 Jarque-Bera (JB): 0.901
Skew: 0.417 Prob(JB): 0.637
Kurtosis: 2.241 Cond. No. 164.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=17
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
Default & er40_cr
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
OLS Regression Results
==============================================================================
Dep. Variable: er40_cr R-squared: 0.038
Model: OLS Adj. R-squared: -0.090
Method: Least Squares F-statistic: 0.2968
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.747
Time: 19:10:02 Log-Likelihood: -31.205
No. Observations: 18 AIC: 68.41
Df Residuals: 15 BIC: 71.08
Df Model: 2
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 35.1400 7.562 4.647 0.000 19.023 51.257
avgCEST_Default 0.2557 0.995 0.257 0.801 -1.865 2.377
Default 2.5313 3.869 0.654 0.523 -5.715 10.777
==============================================================================
Omnibus: 3.300 Durbin-Watson: 2.191
Prob(Omnibus): 0.192 Jarque-Bera (JB): 2.083
Skew: -0.833 Prob(JB): 0.353
Kurtosis: 2.992 Cond. No. 169.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Default & medf_pc
OLS Regression Results
==============================================================================
Dep. Variable: medf_pc R-squared: 0.271
Model: OLS Adj. R-squared: 0.167
Method: Least Squares F-statistic: 2.604
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.109
Time: 19:10:03 Log-Likelihood: -56.738
No. Observations: 17 AIC: 119.5
Df Residuals: 14 BIC: 122.0
Df Model: 2
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 136.5065 37.569 3.633 0.003 55.928 217.085
avgCEST_Default -8.3237 4.860 -1.713 0.109 -18.747 2.099
Default 32.5262 18.592 1.749 0.102 -7.349 72.402
==============================================================================
Omnibus: 0.801 Durbin-Watson: 1.960
Prob(Omnibus): 0.670 Jarque-Bera (JB): 0.477
Skew: -0.392 Prob(JB): 0.788
Kurtosis: 2.759 Cond. No. 164.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=17
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
DorsAttn & tap_tot
OLS Regression Results
==============================================================================
Dep. Variable: tap_tot R-squared: 0.116
Model: OLS Adj. R-squared: 0.005
Method: Least Squares F-statistic: 1.049
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.373
Time: 19:10:03 Log-Likelihood: -74.806
No. Observations: 19 AIC: 155.6
Df Residuals: 16 BIC: 158.4
Df Model: 2
Covariance Type: nonrobust
====================================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------------
Intercept 72.3800 24.623 2.940 0.010 20.181 124.579
avgCEST_DorsAttn 4.3225 3.015 1.434 0.171 -2.070 10.715
DorsAttn -6.7606 23.666 -0.286 0.779 -56.931 43.410
==============================================================================
Omnibus: 0.159 Durbin-Watson: 1.634
Prob(Omnibus): 0.924 Jarque-Bera (JB): 0.086
Skew: -0.112 Prob(JB): 0.958
Kurtosis: 2.758 Cond. No. 69.9
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=19
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
DorsAttn & er40_cr
OLS Regression Results
==============================================================================
Dep. Variable: er40_cr R-squared: 0.187
Model: OLS Adj. R-squared: 0.086
Method: Least Squares F-statistic: 1.842
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.191
Time: 19:10:03 Log-Likelihood: -32.014
No. Observations: 19 AIC: 70.03
Df Residuals: 16 BIC: 72.86
Df Model: 2
Covariance Type: nonrobust
====================================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------------
Intercept 37.3622 2.638 14.165 0.000 31.771 42.954
avgCEST_DorsAttn -0.1398 0.318 -0.440 0.666 -0.814 0.534
DorsAttn 4.6626 2.461 1.894 0.076 -0.555 9.880
==============================================================================
Omnibus: 2.403 Durbin-Watson: 2.016
Prob(Omnibus): 0.301 Jarque-Bera (JB): 1.867
Skew: -0.736 Prob(JB): 0.393
Kurtosis: 2.563 Cond. No. 71.5
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=19
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
DorsAttn & medf_pc
OLS Regression Results
==============================================================================
Dep. Variable: medf_pc R-squared: 0.150
Model: OLS Adj. R-squared: 0.043
Method: Least Squares F-statistic: 1.407
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.274
Time: 19:10:03 Log-Likelihood: -65.796
No. Observations: 19 AIC: 137.6
Df Residuals: 16 BIC: 140.4
Df Model: 2
Covariance Type: nonrobust
====================================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------------
Intercept 96.5777 15.325 6.302 0.000 64.090 129.066
avgCEST_DorsAttn -2.7537 1.877 -1.467 0.162 -6.732 1.225
DorsAttn 13.1453 14.730 0.892 0.385 -18.080 44.371
==============================================================================
Omnibus: 1.763 Durbin-Watson: 2.939
Prob(Omnibus): 0.414 Jarque-Bera (JB): 0.993
Skew: 0.136 Prob(JB): 0.609
Kurtosis: 1.913 Cond. No. 69.9
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=19
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
Vis & tap_tot
OLS Regression Results
==============================================================================
Dep. Variable: tap_tot R-squared: 0.068
Model: OLS Adj. R-squared: -0.056
Method: Least Squares F-statistic: 0.5497
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.588
Time: 19:10:04 Log-Likelihood: -71.408
No. Observations: 18 AIC: 148.8
Df Residuals: 15 BIC: 151.5
Df Model: 2
Covariance Type: nonrobust
===============================================================================
coef std err t P>|t| [0.025 0.975]
-------------------------------------------------------------------------------
Intercept 122.9184 21.652 5.677 0.000 76.767 169.069
avgCEST_Vis -1.0367 1.845 -0.562 0.582 -4.969 2.896
Vis -15.1813 17.562 -0.864 0.401 -52.613 22.250
==============================================================================
Omnibus: 2.237 Durbin-Watson: 1.989
Prob(Omnibus): 0.327 Jarque-Bera (JB): 0.771
Skew: -0.431 Prob(JB): 0.680
Kurtosis: 3.534 Cond. No. 78.3
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
Vis & er40_cr
OLS Regression Results
==============================================================================
Dep. Variable: er40_cr R-squared: 0.048
Model: OLS Adj. R-squared: -0.079
Method: Least Squares F-statistic: 0.3743
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.694
Time: 19:10:04 Log-Likelihood: -27.608
No. Observations: 18 AIC: 61.22
Df Residuals: 15 BIC: 63.89
Df Model: 2
Covariance Type: nonrobust
===============================================================================
coef std err t P>|t| [0.025 0.975]
-------------------------------------------------------------------------------
Intercept 36.5451 2.017 18.118 0.000 32.246 40.844
avgCEST_Vis 0.0818 0.155 0.527 0.606 -0.249 0.413
Vis 1.5206 2.042 0.745 0.468 -2.832 5.873
==============================================================================
Omnibus: 0.284 Durbin-Watson: 1.466
Prob(Omnibus): 0.868 Jarque-Bera (JB): 0.436
Skew: -0.216 Prob(JB): 0.804
Kurtosis: 2.371 Cond. No. 95.5
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
Vis & medf_pc
OLS Regression Results
==============================================================================
Dep. Variable: medf_pc R-squared: 0.083
Model: OLS Adj. R-squared: -0.039
Method: Least Squares F-statistic: 0.6777
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.523
Time: 19:10:04 Log-Likelihood: -62.765
No. Observations: 18 AIC: 131.5
Df Residuals: 15 BIC: 134.2
Df Model: 2
Covariance Type: nonrobust
===============================================================================
coef std err t P>|t| [0.025 0.975]
-------------------------------------------------------------------------------
Intercept 80.9322 13.396 6.041 0.000 52.379 109.485
avgCEST_Vis 0.4233 1.141 0.371 0.716 -2.010 2.856
Vis -12.1276 10.865 -1.116 0.282 -35.286 11.031
==============================================================================
Omnibus: 1.339 Durbin-Watson: 2.564
Prob(Omnibus): 0.512 Jarque-Bera (JB): 1.083
Skew: -0.401 Prob(JB): 0.582
Kurtosis: 2.105 Cond. No. 78.3
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
SalVentAttn & tap_tot
OLS Regression Results
==============================================================================
Dep. Variable: tap_tot R-squared: 0.383
Model: OLS Adj. R-squared: 0.306
Method: Least Squares F-statistic: 4.960
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.0211
Time: 19:10:04 Log-Likelihood: -71.393
No. Observations: 19 AIC: 148.8
Df Residuals: 16 BIC: 151.6
Df Model: 2
Covariance Type: nonrobust
=======================================================================================
coef std err t P>|t| [0.025 0.975]
---------------------------------------------------------------------------------------
Intercept -62.5973 52.941 -1.182 0.254 -174.828 49.633
avgCEST_SalVentAttn 19.1111 6.165 3.100 0.007 6.042 32.180
SalVentAttn 44.1274 27.338 1.614 0.126 -13.826 102.081
==============================================================================
Omnibus: 0.557 Durbin-Watson: 2.088
Prob(Omnibus): 0.757 Jarque-Bera (JB): 0.586
Skew: 0.017 Prob(JB): 0.746
Kurtosis: 2.140 Cond. No. 170.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=19
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
SalVentAttn & er40_cr
OLS Regression Results
==============================================================================
Dep. Variable: er40_cr R-squared: 0.031
Model: OLS Adj. R-squared: -0.090
Method: Least Squares F-statistic: 0.2542
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.779
Time: 19:10:05 Log-Likelihood: -33.685
No. Observations: 19 AIC: 73.37
Df Residuals: 16 BIC: 76.20
Df Model: 2
Covariance Type: nonrobust
=======================================================================================
coef std err t P>|t| [0.025 0.975]
---------------------------------------------------------------------------------------
Intercept 34.5961 8.006 4.321 0.001 17.624 51.569
avgCEST_SalVentAttn 0.2868 0.909 0.316 0.756 -1.639 2.213
SalVentAttn 2.8508 3.998 0.713 0.486 -5.626 11.327
==============================================================================
Omnibus: 2.990 Durbin-Watson: 1.957
Prob(Omnibus): 0.224 Jarque-Bera (JB): 2.020
Skew: -0.796 Prob(JB): 0.364
Kurtosis: 2.876 Cond. No. 191.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=19
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
SalVentAttn & medf_pc
OLS Regression Results
==============================================================================
Dep. Variable: medf_pc R-squared: 0.083
Model: OLS Adj. R-squared: -0.032
Method: Least Squares F-statistic: 0.7200
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.502
Time: 19:10:05 Log-Likelihood: -66.516
No. Observations: 19 AIC: 139.0
Df Residuals: 16 BIC: 141.9
Df Model: 2
Covariance Type: nonrobust
=======================================================================================
coef std err t P>|t| [0.025 0.975]
---------------------------------------------------------------------------------------
Intercept 128.3183 40.957 3.133 0.006 41.494 215.142
avgCEST_SalVentAttn -5.5351 4.769 -1.161 0.263 -15.645 4.575
SalVentAttn -14.6959 21.149 -0.695 0.497 -59.530 30.138
==============================================================================
Omnibus: 4.628 Durbin-Watson: 2.466
Prob(Omnibus): 0.099 Jarque-Bera (JB): 1.461
Skew: -0.019 Prob(JB): 0.482
Kurtosis: 1.642 Cond. No. 170.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=19
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
SomMot & tap_tot
OLS Regression Results
==============================================================================
Dep. Variable: tap_tot R-squared: 0.265
Model: OLS Adj. R-squared: 0.167
Method: Least Squares F-statistic: 2.703
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.0994
Time: 19:10:05 Log-Likelihood: -69.694
No. Observations: 18 AIC: 145.4
Df Residuals: 15 BIC: 148.1
Df Model: 2
Covariance Type: nonrobust
==================================================================================
coef std err t P>|t| [0.025 0.975]
----------------------------------------------------------------------------------
Intercept -5.2784 47.966 -0.110 0.914 -107.515 96.958
avgCEST_SomMot 13.0905 5.633 2.324 0.035 1.084 25.097
SomMot 17.6375 26.561 0.664 0.517 -38.975 74.250
==============================================================================
Omnibus: 0.790 Durbin-Watson: 1.925
Prob(Omnibus): 0.674 Jarque-Bera (JB): 0.433
Skew: 0.369 Prob(JB): 0.805
Kurtosis: 2.816 Cond. No. 132.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
SomMot & er40_cr
OLS Regression Results
==============================================================================
Dep. Variable: er40_cr R-squared: 0.210
Model: OLS Adj. R-squared: 0.104
Method: Least Squares F-statistic: 1.989
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.171
Time: 19:10:05 Log-Likelihood: -30.560
No. Observations: 18 AIC: 67.12
Df Residuals: 15 BIC: 69.79
Df Model: 2
Covariance Type: nonrobust
==================================================================================
coef std err t P>|t| [0.025 0.975]
----------------------------------------------------------------------------------
Intercept 40.0298 5.453 7.340 0.000 28.406 51.653
avgCEST_SomMot -0.5365 0.643 -0.834 0.417 -1.908 0.835
SomMot 4.9555 2.981 1.662 0.117 -1.398 11.309
==============================================================================
Omnibus: 0.156 Durbin-Watson: 2.094
Prob(Omnibus): 0.925 Jarque-Bera (JB): 0.266
Skew: -0.183 Prob(JB): 0.876
Kurtosis: 2.530 Cond. No. 132.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
SomMot & medf_pc
OLS Regression Results
==============================================================================
Dep. Variable: medf_pc R-squared: 0.035
Model: OLS Adj. R-squared: -0.093
Method: Least Squares F-statistic: 0.2757
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.763
Time: 19:10:06 Log-Likelihood: -63.285
No. Observations: 18 AIC: 132.6
Df Residuals: 15 BIC: 135.2
Df Model: 2
Covariance Type: nonrobust
==================================================================================
coef std err t P>|t| [0.025 0.975]
----------------------------------------------------------------------------------
Intercept 95.1220 33.596 2.831 0.013 23.513 166.731
avgCEST_SomMot -2.3619 3.945 -0.599 0.558 -10.772 6.048
SomMot 5.0659 18.604 0.272 0.789 -34.587 44.719
==============================================================================
Omnibus: 4.345 Durbin-Watson: 2.435
Prob(Omnibus): 0.114 Jarque-Bera (JB): 1.407
Skew: -0.068 Prob(JB): 0.495
Kurtosis: 1.637 Cond. No. 132.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
Limbic & tap_tot
OLS Regression Results
==============================================================================
Dep. Variable: tap_tot R-squared: 0.482
Model: OLS Adj. R-squared: 0.352
Method: Least Squares F-statistic: 3.716
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.0722
Time: 19:10:06 Log-Likelihood: -40.228
No. Observations: 11 AIC: 86.46
Df Residuals: 8 BIC: 87.65
Df Model: 2
Covariance Type: nonrobust
==================================================================================
coef std err t P>|t| [0.025 0.975]
----------------------------------------------------------------------------------
Intercept 97.8589 14.960 6.542 0.000 63.362 132.356
avgCEST_Limbic -4.3827 1.975 -2.220 0.057 -8.936 0.171
Limbic 79.2752 33.219 2.386 0.044 2.672 155.879
==============================================================================
Omnibus: 1.519 Durbin-Watson: 1.950
Prob(Omnibus): 0.468 Jarque-Bera (JB): 0.766
Skew: 0.629 Prob(JB): 0.682
Kurtosis: 2.702 Cond. No. 73.9
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=11
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
Limbic & er40_cr
OLS Regression Results
==============================================================================
Dep. Variable: er40_cr R-squared: 0.544
Model: OLS Adj. R-squared: 0.430
Method: Least Squares F-statistic: 4.774
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.0432
Time: 19:10:06 Log-Likelihood: -16.568
No. Observations: 11 AIC: 39.14
Df Residuals: 8 BIC: 40.33
Df Model: 2
Covariance Type: nonrobust
==================================================================================
coef std err t P>|t| [0.025 0.975]
----------------------------------------------------------------------------------
Intercept 33.4960 1.789 18.719 0.000 29.370 37.622
avgCEST_Limbic -0.1002 0.334 -0.300 0.772 -0.870 0.670
Limbic 10.3767 3.987 2.603 0.031 1.184 19.570
==============================================================================
Omnibus: 1.586 Durbin-Watson: 1.953
Prob(Omnibus): 0.453 Jarque-Bera (JB): 1.081
Skew: 0.699 Prob(JB): 0.583
Kurtosis: 2.366 Cond. No. 69.8
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=11
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
Limbic & medf_pc
OLS Regression Results
==============================================================================
Dep. Variable: medf_pc R-squared: 0.313
Model: OLS Adj. R-squared: 0.142
Method: Least Squares F-statistic: 1.826
Date: Tue, 16 Jan 2024 Prob (F-statistic): 0.222
Time: 19:10:07 Log-Likelihood: -36.007
No. Observations: 11 AIC: 78.01
Df Residuals: 8 BIC: 79.21
Df Model: 2
Covariance Type: nonrobust
==================================================================================
coef std err t P>|t| [0.025 0.975]
----------------------------------------------------------------------------------
Intercept 67.3799 10.192 6.611 0.000 43.878 90.882
avgCEST_Limbic 2.5443 1.345 1.891 0.095 -0.558 5.646
Limbic -12.9089 22.632 -0.570 0.584 -65.098 39.280
==============================================================================
Omnibus: 1.436 Durbin-Watson: 2.083
Prob(Omnibus): 0.488 Jarque-Bera (JB): 0.928
Skew: -0.414 Prob(JB): 0.629
Kurtosis: 1.842 Cond. No. 73.9
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=11
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
#BBS2
from sklearn import linear_model
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
import matplotlib.pyplot as plt
if run_grpanalysis:
# Curate data
value_counts = grp_df['hstatus'].value_counts()
print(value_counts)
grp_df['hstatus'] = grp_df['hstatus'].replace('NC', 'HC')
grp_df['hstatus'] = grp_df['hstatus'].replace('PROR', 'PSY')
grp_df['hstatus'] = grp_df['hstatus'].replace('PRO', 'PSY')
grp_df['hstatus'] = grp_df['hstatus'].replace('S', 'PSY')
grp_df['hstatus'] = grp_df['hstatus'].replace('O', 'Other')
grp_df['hstatus'] = grp_df['hstatus'].replace('Unknown', 'Other')
grp_df['hstatus'] = grp_df['hstatus'].replace('MDD', 'Other')
value_counts = grp_df['hstatus'].value_counts()
print(value_counts)
colors = pd.DataFrame({'Network': ["Cont", "Default", "DorsAttn", "Vis", "SalVentAttn", "SomMot", "Limbic"],
'Color': ['PuOr', 'PuRd_r', 'PiYG_r', 'PRGn', 'PiYG', 'GnBu_r', 'terrain_r']}) #
anova_tables = []
# Create a scatter plot with a multiple linear regression
for network in networks:
cestcol = "avgCEST_" + network
# Create a linear regression model for fcon
# color = colors.loc[colors['Network'] == network, 'Color'].values[0]
# sns.set_palette(color)
# Create CNB correlation plot for each network fcon and cest
for CNB_score in CNB_scores:
# graph_df = grp_df[grp_df['hstatus'] != 'Other']
graph_df = grp_df
graph_df = graph_df.dropna(subset=[CNB_score, cestcol, network])
graph_df = graph_df[[CNB_score, cestcol, network]]
# Define x values and target variable
X = graph_df[[cestcol, network]]
Y = graph_df[CNB_score]
##################################
# Define formula and model
#formula = f'{CNB_score} ~ {cestcol} + {network}'
#model = smf.ols(formula=formula, data=graph_df).fit()
#print(network + CNB_score)
#print(model.summary())
print(graph_df)
fig = plt.figure()
ax = fig.add_subplot(111, projection = '3d')
ax.scatter(graph_df[CNB_score], graph_df[cestcol], graph_df[network])
ax.set_xlabel(CNB_score)
ax.set_ylabel(cestcol)
ax.set_zlabel(network)
plt.show()
hstatus
HC 10
Other 10
PSY 9
Name: count, dtype: int64
hstatus
HC 10
Other 10
PSY 9
Name: count, dtype: int64
tap_tot avgCEST_Cont Cont
3 103.33330 8.684751 0.121458
7 92.00000 8.109159 0.096672
10 105.33330 8.515892 0.246400
13 116.33330 9.195672 0.222760
14 92.66666 7.579884 0.341071
15 93.33333 8.099021 0.165071
16 117.00000 7.687283 0.169243
17 103.33330 8.099098 0.261861
18 128.00000 8.151035 0.231845
19 120.00000 7.851769 0.201449
24 96.33333 8.402123 0.233773
25 110.00000 8.444942 0.201716
26 101.66670 8.653494 0.243740
27 92.00000 7.956718 0.119005
30 104.66670 8.590144 0.102177
31 125.66670 8.391536 0.128059
er40_cr avgCEST_Cont Cont 0 39.0 8.496428 0.175604 3 38.0 8.684751 0.121458 4 39.0 8.097737 0.248519 7 34.0 8.109159 0.096672 10 39.0 8.515892 0.246400 13 37.0 9.195672 0.222760 15 37.0 8.099021 0.165071 16 37.0 7.687283 0.169243 17 36.0 8.099098 0.261861 18 38.0 8.151035 0.231845 19 38.0 7.851769 0.201449 24 40.0 8.402123 0.233773 25 38.0 8.444942 0.201716 26 38.0 8.653494 0.243740 27 40.0 7.956718 0.119005 30 39.0 8.590144 0.102177 31 36.0 8.391536 0.128059
medf_pc avgCEST_Cont Cont 3 77.777778 8.684751 0.121458 7 69.444444 8.109159 0.096672 10 77.777778 8.515892 0.246400 13 69.444444 9.195672 0.222760 14 72.222222 7.579884 0.341071 15 80.555556 8.099021 0.165071 16 86.111111 7.687283 0.169243 17 86.111111 8.099098 0.261861 18 66.666667 8.151035 0.231845 19 91.666667 7.851769 0.201449 24 69.444444 8.402123 0.233773 25 88.888889 8.444942 0.201716 26 75.000000 8.653494 0.243740 27 80.555556 7.956718 0.119005 30 83.333333 8.590144 0.102177 31 86.111111 8.391536 0.128059
tap_tot avgCEST_Default Default 2 104.00000 7.469096 0.260692 3 103.33330 8.128972 0.322248 7 92.00000 7.698660 0.101490 10 105.33330 7.951705 0.168437 11 97.00000 7.705413 0.235045 13 116.33330 8.244459 0.233656 14 92.66666 8.285107 0.069097 15 93.33333 7.263857 0.196767 16 117.00000 7.310467 0.402548 17 103.33330 7.649832 0.323492 18 128.00000 7.537559 0.195510 19 120.00000 7.571108 0.240199 25 110.00000 7.758677 0.156970 26 101.66670 8.420564 0.267923 27 92.00000 8.445907 0.399663 30 104.66670 7.303762 0.097644 31 125.66670 7.966794 0.364319
er40_cr avgCEST_Default Default 0 39.0 7.605122 0.376170 2 38.0 7.469096 0.260692 3 38.0 8.128972 0.322248 4 39.0 7.356575 0.331945 7 34.0 7.698660 0.101490 10 39.0 7.951705 0.168437 11 39.0 7.705413 0.235045 13 37.0 8.244459 0.233656 15 37.0 7.263857 0.196767 16 37.0 7.310467 0.402548 17 36.0 7.649832 0.323492 18 38.0 7.537559 0.195510 19 38.0 7.571108 0.240199 25 38.0 7.758677 0.156970 26 38.0 8.420564 0.267923 27 40.0 8.445907 0.399663 30 39.0 7.303762 0.097644 31 36.0 7.966794 0.364319
medf_pc avgCEST_Default Default 2 88.888889 7.469096 0.260692 3 77.777778 8.128972 0.322248 7 69.444444 7.698660 0.101490 10 77.777778 7.951705 0.168437 11 66.666667 7.705413 0.235045 13 69.444444 8.244459 0.233656 14 72.222222 8.285107 0.069097 15 80.555556 7.263857 0.196767 16 86.111111 7.310467 0.402548 17 86.111111 7.649832 0.323492 18 66.666667 7.537559 0.195510 19 91.666667 7.571108 0.240199 25 88.888889 7.758677 0.156970 26 75.000000 8.420564 0.267923 27 80.555556 8.445907 0.399663 30 83.333333 7.303762 0.097644 31 86.111111 7.966794 0.364319
tap_tot avgCEST_DorsAttn DorsAttn 2 104.00000 6.382527 0.258377 3 103.33330 6.035932 0.287490 5 72.33333 6.972563 0.316759 7 92.00000 8.559614 0.063928 10 105.33330 8.223989 0.243422 11 97.00000 8.287902 0.429669 13 116.33330 10.419630 0.278428 14 92.66666 7.080233 0.176538 15 93.33333 7.078968 0.330240 16 117.00000 6.118033 0.309288 17 103.33330 7.992198 0.510831 18 128.00000 8.277535 0.200455 19 120.00000 8.579904 0.335207 24 96.33333 8.066102 0.401307 25 110.00000 7.902069 0.219597 26 101.66670 7.363532 0.494674 27 92.00000 8.043687 0.631075 30 104.66670 8.293079 0.313409 31 125.66670 8.892468 0.473277
er40_cr avgCEST_DorsAttn DorsAttn 0 39.0 7.654973 0.402285 2 38.0 6.382527 0.258377 3 38.0 6.035932 0.287490 4 39.0 9.062175 0.520766 7 34.0 8.559614 0.063928 10 39.0 8.223989 0.243422 11 39.0 8.287902 0.429669 13 37.0 10.419630 0.278428 15 37.0 7.078968 0.330240 16 37.0 6.118033 0.309288 17 36.0 7.992198 0.510831 18 38.0 8.277535 0.200455 19 38.0 8.579904 0.335207 24 40.0 8.066102 0.401307 25 38.0 7.902069 0.219597 26 38.0 7.363532 0.494674 27 40.0 8.043687 0.631075 30 39.0 8.293079 0.313409 31 36.0 8.892468 0.473277
medf_pc avgCEST_DorsAttn DorsAttn 2 88.888889 6.382527 0.258377 3 77.777778 6.035932 0.287490 5 91.666667 6.972563 0.316759 7 69.444444 8.559614 0.063928 10 77.777778 8.223989 0.243422 11 66.666667 8.287902 0.429669 13 69.444444 10.419630 0.278428 14 72.222222 7.080233 0.176538 15 80.555556 7.078968 0.330240 16 86.111111 6.118033 0.309288 17 86.111111 7.992198 0.510831 18 66.666667 8.277535 0.200455 19 91.666667 8.579904 0.335207 24 69.444444 8.066102 0.401307 25 88.888889 7.902069 0.219597 26 75.000000 7.363532 0.494674 27 80.555556 8.043687 0.631075 30 83.333333 8.293079 0.313409 31 86.111111 8.892468 0.473277
tap_tot avgCEST_Vis Vis 2 104.00000 8.967306 0.396056 3 103.33330 9.610587 0.468854 5 72.33333 12.741945 0.274933 10 105.33330 9.746861 0.738115 11 97.00000 10.589238 0.509295 13 116.33330 12.802977 0.492354 14 92.66666 13.682704 0.967040 15 93.33333 9.176037 0.793801 16 117.00000 9.737379 0.309563 17 103.33330 10.190264 0.354786 18 128.00000 9.714172 0.335023 19 120.00000 10.088696 0.553575 24 96.33333 10.633214 0.342325 25 110.00000 12.498565 0.352182 26 101.66670 7.216132 0.351959 27 92.00000 12.373299 0.484798 30 104.66670 13.772174 0.297944 31 125.66670 12.511567 0.295938
er40_cr avgCEST_Vis Vis 0 39.0 14.297329 0.594154 2 38.0 8.967306 0.396056 3 38.0 9.610587 0.468854 4 39.0 8.387602 0.525277 10 39.0 9.746861 0.738115 11 39.0 10.589238 0.509295 13 37.0 12.802977 0.492354 15 37.0 9.176037 0.793801 16 37.0 9.737379 0.309563 17 36.0 10.190264 0.354786 18 38.0 9.714172 0.335023 19 38.0 10.088696 0.553575 24 40.0 10.633214 0.342325 25 38.0 12.498565 0.352182 26 38.0 7.216132 0.351959 27 40.0 12.373299 0.484798 30 39.0 13.772174 0.297944 31 36.0 12.511567 0.295938
medf_pc avgCEST_Vis Vis 2 88.888889 8.967306 0.396056 3 77.777778 9.610587 0.468854 5 91.666667 12.741945 0.274933 10 77.777778 9.746861 0.738115 11 66.666667 10.589238 0.509295 13 69.444444 12.802977 0.492354 14 72.222222 13.682704 0.967040 15 80.555556 9.176037 0.793801 16 86.111111 9.737379 0.309563 17 86.111111 10.190264 0.354786 18 66.666667 9.714172 0.335023 19 91.666667 10.088696 0.553575 24 69.444444 10.633214 0.342325 25 88.888889 12.498565 0.352182 26 75.000000 7.216132 0.351959 27 80.555556 12.373299 0.484798 30 83.333333 13.772174 0.297944 31 86.111111 12.511567 0.295938
tap_tot avgCEST_SalVentAttn SalVentAttn 2 104.00000 7.277624 0.420864 3 103.33330 7.863291 0.243196 5 72.33333 7.181154 0.302949 7 92.00000 8.124917 0.186044 10 105.33330 7.937581 0.340396 11 97.00000 7.662420 0.424431 13 116.33330 8.939010 0.295722 14 92.66666 7.325671 0.505170 15 93.33333 7.404367 0.378813 16 117.00000 7.365407 0.467500 17 103.33330 7.774126 0.482222 18 128.00000 8.103207 0.437835 19 120.00000 7.989862 0.349883 24 96.33333 7.920916 0.533080 25 110.00000 8.152667 0.318753 26 101.66670 7.594341 0.273180 27 92.00000 8.100647 0.392901 30 104.66670 8.358057 0.159922 31 125.66670 8.547472 0.396681
er40_cr avgCEST_SalVentAttn SalVentAttn 0 39.0 8.034846 0.371115 2 38.0 7.277624 0.420864 3 38.0 7.863291 0.243196 4 39.0 8.805663 0.222687 7 34.0 8.124917 0.186044 10 39.0 7.937581 0.340396 11 39.0 7.662420 0.424431 13 37.0 8.939010 0.295722 15 37.0 7.404367 0.378813 16 37.0 7.365407 0.467500 17 36.0 7.774126 0.482222 18 38.0 8.103207 0.437835 19 38.0 7.989862 0.349883 24 40.0 7.920916 0.533080 25 38.0 8.152667 0.318753 26 38.0 7.594341 0.273180 27 40.0 8.100647 0.392901 30 39.0 8.358057 0.159922 31 36.0 8.547472 0.396681
medf_pc avgCEST_SalVentAttn SalVentAttn 2 88.888889 7.277624 0.420864 3 77.777778 7.863291 0.243196 5 91.666667 7.181154 0.302949 7 69.444444 8.124917 0.186044 10 77.777778 7.937581 0.340396 11 66.666667 7.662420 0.424431 13 69.444444 8.939010 0.295722 14 72.222222 7.325671 0.505170 15 80.555556 7.404367 0.378813 16 86.111111 7.365407 0.467500 17 86.111111 7.774126 0.482222 18 66.666667 8.103207 0.437835 19 91.666667 7.989862 0.349883 24 69.444444 7.920916 0.533080 25 88.888889 8.152667 0.318753 26 75.000000 7.594341 0.273180 27 80.555556 8.100647 0.392901 30 83.333333 8.358057 0.159922 31 86.111111 8.547472 0.396681
tap_tot avgCEST_SomMot SomMot 3 103.33330 8.048849 0.476986 5 72.33333 6.923363 0.530501 7 92.00000 8.360912 0.207787 10 105.33330 7.416647 0.458101 11 97.00000 7.336883 0.495047 13 116.33330 8.874376 0.438165 14 92.66666 7.077155 0.330849 15 93.33333 7.627305 0.276265 16 117.00000 6.892677 0.389875 17 103.33330 8.036966 0.519093 18 128.00000 8.222816 0.371118 19 120.00000 7.866533 0.616235 24 96.33333 7.681239 0.631874 25 110.00000 7.964384 0.323271 26 101.66670 7.400841 0.457556 27 92.00000 7.430732 0.372234 30 104.66670 8.525817 0.221894 31 125.66670 8.374417 0.397562
er40_cr avgCEST_SomMot SomMot 0 39.0 7.723693 0.504323 3 38.0 8.048849 0.476986 4 39.0 8.941838 0.517550 7 34.0 8.360912 0.207787 10 39.0 7.416647 0.458101 11 39.0 7.336883 0.495047 13 37.0 8.874376 0.438165 15 37.0 7.627305 0.276265 16 37.0 6.892677 0.389875 17 36.0 8.036966 0.519093 18 38.0 8.222816 0.371118 19 38.0 7.866533 0.616235 24 40.0 7.681239 0.631874 25 38.0 7.964384 0.323271 26 38.0 7.400841 0.457556 27 40.0 7.430732 0.372234 30 39.0 8.525817 0.221894 31 36.0 8.374417 0.397562
medf_pc avgCEST_SomMot SomMot 3 77.777778 8.048849 0.476986 5 91.666667 6.923363 0.530501 7 69.444444 8.360912 0.207787 10 77.777778 7.416647 0.458101 11 66.666667 7.336883 0.495047 13 69.444444 8.874376 0.438165 14 72.222222 7.077155 0.330849 15 80.555556 7.627305 0.276265 16 86.111111 6.892677 0.389875 17 86.111111 8.036966 0.519093 18 66.666667 8.222816 0.371118 19 91.666667 7.866533 0.616235 24 69.444444 7.681239 0.631874 25 88.888889 7.964384 0.323271 26 75.000000 7.400841 0.457556 27 80.555556 7.430732 0.372234 30 83.333333 8.525817 0.221894 31 86.111111 8.374417 0.397562
tap_tot avgCEST_Limbic Limbic 2 104.00000 10.076208 0.556081 3 103.33330 5.855585 0.557474 5 72.33333 10.419856 0.355055 7 92.00000 5.411261 0.219353 10 105.33330 7.552845 0.553482 13 116.33330 6.410949 0.470029 15 93.33333 4.421310 0.308839 17 103.33330 6.229536 0.343770 24 96.33333 7.509117 0.488638 26 101.66670 4.831669 0.323012 31 125.66670 6.177131 0.434284
er40_cr avgCEST_Limbic Limbic 2 38.0 10.076208 0.556081 3 38.0 5.855585 0.557474 4 39.0 6.432389 0.625961 7 34.0 5.411261 0.219353 10 39.0 7.552845 0.553482 13 37.0 6.410949 0.470029 15 37.0 4.421310 0.308839 17 36.0 6.229536 0.343770 24 40.0 7.509117 0.488638 26 38.0 4.831669 0.323012 31 36.0 6.177131 0.434284
medf_pc avgCEST_Limbic Limbic 2 88.888889 10.076208 0.556081 3 77.777778 5.855585 0.557474 5 91.666667 10.419856 0.355055 7 69.444444 5.411261 0.219353 10 77.777778 7.552845 0.553482 13 69.444444 6.410949 0.470029 15 80.555556 4.421310 0.308839 17 86.111111 6.229536 0.343770 24 69.444444 7.509117 0.488638 26 75.000000 4.831669 0.323012 31 86.111111 6.177131 0.434284
!jupyter nbconvert --to html motor_pipeline.ipynb --output motor_pipeline_3T.html